rp-884 final report - the university of sydney
TRANSCRIPT
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Developing an Adaptive Model of Thermal Comfort and Preference
FINAL REPORT
ASHRAE RP- 884
March 1997
Richard de DearÀ, Gail BragerÁ, Donna CooperÀ
À Macquarie Research Ltd., Macquarie University, Sydney, NSW 2109 AUSTRALIA
Á Center for Environmental Design Research, University of California,
Berkeley, CA 94720 USA
“Results of Cooperative Research between the American Society of Heating, Refrigerating
and Air Conditioning Engineers, Inc., and Macquarie Research, Ltd.”
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TABLE OF CONTENTS iii
ACKNOWLEDGMENTS vii
EXECUTIVE SUMMARY ix
CHAPTER 1 - INTRODUCTION & BACKGROUND 1
1.1. Introduction 1
1.2. Defining the adaptive process 3 1.2.1. The dialectic of contemporary thermal comfort theory 3 1.2.2. The “adaptive” hypothesis 4
1.3. A conceptual model of adaptation -- feedback loops 6 1.3.1. Behavioral feedback - adjustment 8 1.3.2. Physiological feedback -- acclimatization 10 1.3.3. Psychological feedback -- habituation and expectation 12
1.4. Literature review 13 1.4.1. Climate chamber evidence for adaptation to climate 13 1.4.2. Field evidence for adaptation 15
1.4.2.1. The earlier field evidence for adaptation 16 1.4.2.2. Analysis of neutral temperatures using recent field experiments 18 1.4.2.3. Evidence for behavioral adaptation - personal/environmental adjustment 22 1.4.2.4. Evidence for psychological adaptation - expectation and context 23
1.5. Implications for RP-884 26 1.5.1. Lessons from static heat balance models 26 1.5.2. Time scales of thermal adaptation 29
1.6. Aims 31
CHAPTER 2 - METHODS 33
2.1. Overview of the RP-884 approach 33
2.2. Establishing the database for RP-884 36 2.2.1. Sourcing the raw data 36 2.2.2. Ratings of raw data submitted to RP-884 40
2.3. Raw data standardisation 41 2.3.1. Creation of a standard data template 41 2.3.2. Consistent mean radiant temperatures within the database. 42 2.3.3. Consistent comfort index calculations within the database 42 2.3.4. Predicted draft risk index (PD) 43 2.3.5. Clothing insulation in the ASHRAE RP-884 database 44
2.3.5.1. Discrepancies between field estimation methods for clo. 45 2.3.5.2. The chair insulation effect 49
2.4. Developing an index for perceived thermal control 49
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2.5. Thermal acceptability issues within the RP-884 database 51 2.5.1. Developing a proxy variable for thermal acceptability based on thermal
sensation votes. 51 2.5.2. Rating buildings in terms of their compliance with ASHRAE Standard 55
acceptable indoor climate guidelines 52
2.6. Outdoor meteorological/climatological data for the data base 52 2.6.1. Appending outdoor weather observations to each row of data 52 2.6.2. Climate classification applied to RP-884 raw data 53
2.7. Subdivision of the standardized field experiments 54
2.8. The meta-analysis 54 2.8.1. The unit of analysis for the RP-884 meta-analysis 54 2.8.2. Meta-file’s structure and coding conventions 55 2.8.3. General assumptions within the statistical meta-analysis 55 2.8.4. Statistical treatments on the various subjective thermal ratings 56 2.8.5. Preferred temperatures 59
2.9. The RP-884 database in the public domain and disseminated via the world wide web 60
2.10. Summary of the methods used in RP-884 64
CHAPTER 3 - BASIC RESULTS 67
3.1. Interactions with indoor climate 67 3.1.1. Thermal sensation 67
3.1.1.1. Dependence of thermal sensation on indoor operative temperature 68 3.1.1.2. Dependence of thermal sensation on indoor ET 69 3.1.1.3. Dependence of thermal sensation on PMV 70 3.1.1.4. Dependence of thermal sensation on indoor SET 71
3.1.2. Thermal neutrality 72 3.1.2.1. Neutral operative temperatures (neut_top) 72 3.1.2.2. Neutral effective temperatures (neut_et) 74 3.1.2.3. Neutral predicted mean votes (neut_pmv) 74 3.1.2.4. Predicted neutralities with the PMV heat balance model 75 3.1.2.5. Neutral standard effective temperatures (neut_set) 77
3.1.3. Thermal acceptability and indoor climate 78 3.1.3.1. Relationship between direct and inferred thermal acceptability 78 3.1.3.2. Directly determined thermal acceptability 80 3.1.3.3. Thermal acceptability inferred from thermal sensation 83 3.1.3.4. Thermal sensitivity and the range of thermally acceptable temperatures. 84
3.1.4. Thermal preferences and indoor climate 89 3.1.5. Comparisons between neutral and preferred temperatures indoors. 91 3.1.6. Behavioural adjustments to indoor climate 93
3.1.6.1. Thermal insulation adjustments indoors 94 3.1.6.2. Metabolic rate adjustments indoors 97 3.1.6.3. Air speed adjustments indoors 99
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3.2. Interactions with outdoor weather and climate 102 3.2.1. Thermal neutrality and outdoor climate 102
3.2.1.1. Seasonal comparisons 103 3.2.1.2. Dependence of observed neutrality on outdoor climate 104 3.2.1.3. Analysis of predicted neutralities with respect to mean outdoor temperature 106
3.2.2. Thermal acceptability and outdoor climate 108 3.2.3. Thermal preference and outdoor climate 110 3.2.4. Behavioral responses to outdoor climate 113
3.2.4.1. Indoor clothing and outdoor climate 114 3.2.4.2. Metabolic rate indoors related to outdoor climate 115 3.2.4.3. Indoor air speeds in relation to outdoor climate 116
3.3. Influence of building characteristics on thermal comfort 118 3.3.1. HVAC versus natural ventilation 118
3.3.1.1. Thermal sensation and sensitivity in HVAC versus naturally ventilated buildings 119
3.3.1.2. Thermal acceptability in HVAC versus naturally ventilated buildings 121 3.3.1.3. Thermal preferences in HVAC versus naturally ventilated buildings. 122
3.3.2. Personal environmental control 124 3.3.3. Building occupancy types - offices, residential and industrial 127
3.4. Summary of basic results 130 3.4.1. Summary of thermal sensation, acceptability and preference 131 3.4.2. Summary of thermal sensitivity and behavioral thermoregulation 133 3.4.3. Summary of the effects of outdoor climate on thermal perception indoors 134 3.4.4. Summary of the effects of contextual factors and perceived control 135
CHAPTER 4 - TOWARDS ADAPTIVE MODELS 139
4.1. The semantics of thermal comfort 139
4.2. Comparison of RP-884 models with earlier adaptive model publications 141
4.3. Comparison of RP-884 models with the PMV “static model” 145 4.3.1. Comparisons within the centrally conditioned building sample 146 4.3.2. Comparisons within the naturally ventilated building sample 150
4.4. Adaptive models for acceptable ranges of indoor temperatures 152
CHAPTER 5 - VARIABLE TEMPERATURE STANDARDS 155
5.1. A variable temperature standard for application in buildings with centrally controlled HVAC 155
5.1.1. Purpose 155 5.1.2. Scope 156 5.1.3. Definitions 156 5.1.4. Conditions for an acceptable thermal environment. 161
5.1.4.1. Analytic PMV method 161 5.1.4.2. Adaptive PMV method 161 5.1.4.3. Prescriptive method 163
5.2. A variable temperature standard for application in naturaly ventilated buildings 165
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5.2.1. Purpose 165 5.2.2. Scope 165 5.2.3. Definitions 166 5.2.4. Conditions for an acceptable thermal environment. 168
BIBLIOGRAPHY 171
APPENDIX A - THERMAL SENSATION AND NEUTRALITY FOR EACH BUILDING IN THE RP-884 DATABASE 185
APPENDIX B - PREFERRED TEMPERATURE FOR EACH BUILDING IN THE RP-884 DATABASE 227
APPENDIX C - SUMMARY OF THE ORIGINAL FIELD EXPERIMENTS COMPRISING THE ASHRAE RP-884 DATABASE 235
C.1. Project Title - ASHRAE TC 2.1 sponsored RP-702 236
C.2. Project Title - Thermal comfort studies in modern industrial buildings. 239
C.3. Project Title - Doctoral dissertation. From comfort to kilowatts: An integrated assessment of electricity conservation in Thailand’s commercial sector. 242
C.4. Project Title - The CSAA, Antioch (1995) component of the Advanced Customer Technology Test (ACT2) project. 245
C.5. Project Title - Higher PMV causes higher energy consumption in air- conditioned buildings: a case study in Jakarta, Indonesia. 248
C.6. Project Title - Montreal ASHRAE RP-821. 250
C.7. Project Title - Richard de Dear’s PhD research project in Australia. 253
C.8. Project Title - A field study of thermal comfort using questionnaire software. 256
C.9. Project Title - “Thermal comfort in Pakistan.” 258
C.10. Project Title - Comfort criteria for passively cooled buildings. a PASCOOL task. 262
C.11. Project Title - Developing indoor temperatures for naturally ventilated buildings. 264
C.12. Project Title - Mixed mode climate control: some hands-on experience. 267
C.13. Project Title - ASHRAE sponsored RP-462. San Francisco area. 269
C.14. Project Title - A field investigation of thermal comfort environmental satisfaction and perceived control levels in UK office buildings, University of Liverpool. 272
C.15. Project Title - Thermal comfort in the humid tropics: field experiments in air conditioned and naturally ventilated buildings in Singapore. 275
C.16. Project Title - The Steelcase building. Grand Rapids Michigan, US 277
C.17. Project Title - Sunset building: a study of occupant thermal comfort in support of PG&E’s Advanced Customer Technology Test (ACT2) for maximum energy efficiency 279
C.18. Project Title - The Verifone building, a component of the Advanced Customer Technology Test (ACT2) Project. 282
APPENDIX D - CLIMATE CLASSIFICATION 285
APPENDIX E - CODEBOOK FOR RAW DATA IN RP-884 DATABASE 287
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APPENDIX F - CODEBOOK FOR THE RP-884 META-ANALYSIS 291
APPENDIX G - AULICIEMS’ ADAPTIVE MODEL DATABASE 295
ACKNOWLEDGMENTS
The successful completion of this project depended very heavily on the willingness of field
researchers to make available their raw data for re-analysis and incorporation into the RP-
884 database. In particular, we would like to thank the following contributors:
Dr. Jill Brown, formerly of University of Wales, Cardiff; Dr. John Busch Jr. Lawrence
Berkeley Labs., California; Prof. Cris Benton, CEDR, University of California at Berkeley;
Dr. Tri Karyono, Agency for the Assessment and Application of Technology (BPPT),
Jakarta, Indonesia (formerly of the Department of Architecture, University of Sheffield, UK);
Dr. Giovanna Donnini, formerly of Auger, Donnini and Nguyen Inc, Montreal, Canada; Dr.
Guy Newsham, Institute for Research in Construction, National Research Council of
Canada, Ottawa; Fergus Nicol, School of Architecture, Oxford-Brookes University, UK.;
Iftikhar Raja, School of Architecture, Oxford-Brookes University, UK; Prof. Nick Baker, The
Martin Centre for Architecture and Urban Studies, University of Cambridge, UK; David
Rowe, Dept. of Architectural and Design Science, University of Sydney, Australia; Dr Ruth
Williams, The Building Services Research and Information Association, UK (formerly
Liverpool University, UK); Fred Bauman, CEDR, University of California at Berkeley.
RP-884 also depended on weather and climate data resources. Such data was required for
the relevant sites and periods covered by field experiments within the database. Apart from
resources available on the WWW and various CD-ROM publications, the following
organisations provided data. The Australian Bureau of Meteorology’s National Climate
Centre supplied meteorological data for the Melbourne, Brisbane and Darwin field
experiments. The Oxford University Radcliffe Observatory supplied meteorological
observations for some of the UK experiments. Macquarie University’s Meteorological Site
supplied observations for the Sydney field data. The US National Climate Data Center
(NCDC) supplied meteorological data for the Californian experiments. Meteorological
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observations for Grand Rapids were supplied by the Michigan State Climatologist.
Bangkok meteorological data were supplied by the Royal Thai Meteorological Department.
Special thanks are also due to Andris Auliciems of the University of Queensland, Fergus
Nicol of Oxford-Brookes University and Michael Humphreys of Oxford University for their
pioneering work in the area of adaptive models and also for their encouragement at various
stages during the ASHRAE RP-884 project.
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EXECUTIVE SUMMARY
One of the more contentious theoretical issues in the applied research area of thermal
comfort has been the dialectic between “adaptive” and “static” models. Apart from having
disparate methodological bases (the former laboratory-experimental, the latter field-based),
the two approaches have yielded starkly differing prescriptions for how the indoor climate of
buildings should be managed. These prescriptions carry implications for the types of
permissible building designs, the means by which their thermal environments are controlled,
and the amounts of energy they consume in the production of habitable indoor climates.
Static models have led to indoor climate standards that have been universally applied
across all building types, are characterised by minimal recognition of outdoor climatic
context, and are contributing to an increased reliance on mechanical cooling. In contrast,
proponents of adaptive models have advocated variable indoor temperature standards that
more fully exercise the adaptive capabilities of building occupants. This approach
potentially leads to more responsive environmental control algorithms, enhanced levels of
occupant comfort, reduced energy consumption, and the encouragement of climatically
responsive building design.
Despite these apparent differences, our review of the research literature emerging from both
approaches indicated that this seemingly irreconcilable split was primarily the result of
narrow definitions of the term “thermal adaptation”, and that there were opportunities to
bridge some of the gap between the hypotheses. We suggest that human thermal
adaptation is comprised of three distinct yet interrelated processes - behavioral,
physiological, and psychological. The adoption of this tripartite definition goes some way
towards reconciling the static and adaptive approaches and the indoor climate standards
derived from them.
This project’s principal objective was the proposal of a variable temperature standard based
on the adaptive approach. Where it differs from earlier attempts is in the quality control
applied throughout its adaptive modelling method. About 21,000 sets of raw thermal
comfort data from 160 buildings were collected from most of the thermal comfort field
research groups around the world who are currently active. Data selection criteria
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emphasized precision of indoor climatic instruments, while data assimilation involved a
variety of questionnaire standardization processes. For example, each one of the over
21,000 building subjects’ clothing thermal insulation estimates was transformed into an
equivalent clo value using consistent procedures specified in ASHRAE Standard 55-1992.
The thermal effects of chairs for seated subjects was also included. For each set of raw
data, outdoor meteorological and climatological data were appended to the RP-884
database. All indoor and outdoor thermal indices were recalculated using a standard
software package (WinComf©) recently commissioned by ASHRAE’s TC 2.1. Since a
significant component of this project’s effort was expended in the assembly of the database,
and since that database has relevance to thermal comfort research problems extending well
beyond the scope of RP-884, we have chosen to place this valuable data resource in the
public domain (World Wide Web) where it can be used by the international thermal comfort
research community.
After statistically analysing the raw data collected in each of the RP-884 database’s 160
buildings, we conducted a meta-analysis of human subjective response to indoor climate
and how it interacted with indoor architectural, contextual and outdoor meteorological
factors. The main subjective response variables were thermal neutrality (derived from
thermal sensation votes) and preferred temperature. Eighty and 90% thermal acceptability
criteria for general thermal comfort were estimated for each building as the range of
operative temperatures falling between mean thermal sensations of ±0.85 and ±0.5
respectively. The list of independent variables in the meta-analysis included the following
indoor climatic indices: operative temperature, effective temperature, PMV/PPD and
standard effective temperature. Outdoor climate was operationalized as an independent
variable in our meta-analysis as the mean of daily minimum and maximum outdoor effective
temperatures prevailing during each building’s survey period. The most important contextual
factor in our meta-analysis was a classification of buildings as having either central HVAC or
being naturally ventilated. This distinction was a unique feature of the ASHRAE RP-884
project, and produced some of the most significant results.
The meta-analysis clearly indicated that the definition and prescription for thermal
acceptability contained in ASHRAE Standard 55-92 bore little resemblance or relationship
to the levels actually expressed by occupants within the building sample. Thermal sensation
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and thermal preference on the other hand, demonstrated statistically significant dependence
on indoor thermal indices prevailing at the time of the questionnaire (these included
operative, effective and standard effective temperatures, or PMV/PPD). Thermal neutrality,
defined as the operative temperature most closely corresponding with a mean thermal
sensation vote of zero (“neutral”) showed an adaptive relationship with mean indoor
temperatures - warm buildings had warm neutralities and vice versa. However, this adaptive
relationship was stronger in naturally ventilated buildings than in buildings with centralized
HVAC systems. Similar adaptive relationships were established for neutrality and
preference with outdoor climate, and again, the strength of the relationship was greater in
the sample of naturally ventilated buildings. These observations support the notion that
building occupants’ thermal ideals are influenced by their thermal experiences both indoors
and outdoors.
Preferred temperature for a particular building did not necessarily coincide with thermal
neutrality, and this semantic discrepancy was most evident in HVAC buildings where
preference was depressed below neutrality in warm climates and elevated above neutrality
in cold climates (i.e, people preferred to feel cooler than neutral in warm climates, and
warmer than neutral in cold climates). This finding suggests that much of what has been
regarded as climatic adaptation by previous proponents of the adaptive model was in fact a
consequence of defining thermal optima in terms of neutrality instead of preference.
Clothing insulation worn by building occupants demonstrated a dependence on both mean
indoor and outdoor temperatures. Thermal insulation levels worn indoors decreased as
indoor and outdoor temperatures increased, while mean indoor air speed demonstrated a
positive dependence on prevailing temperature levels. The close agreement between PMV
model predictions of optimum indoor temperature and those actually observed within HVAC
buildings suggests that the type of thermal adaptation found in such buildings was of the
behavioral type, mainly driven by adjustments to clothing and indoor air speed. In contrast,
the range of optimum indoor temperatures observed in naturally ventilated buildings was
about twice as large as that predicted by the PMV model, suggesting that physiological
(acclimatisation) and psychological (shifting expectations) adaptive processes were
superimposed on the behavioral adaptations of clothing and air speed adjustment in the
naturally ventilated context.
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Based on these adaptive relationships between indoor comfort and outdoor climate, the RP-
884 project concluded with a pair of variable temperature standards. One standard was
designed for use in HVAC buildings where occupants had little or no adaptive opportunity,
while the other was designed for naturally ventilated buildings where occupants had access
to operable windows and other adaptive opportunities. The HVAC standard was based on
three alternative methods; a) the analytic PMV method for use whenever accurate estimates
for all the heat-balance model’s inputs were feasible; b) the modified “adaptive PMV”
method for use whenever an accurate estimate of mean outdoor effective temperature was
possible (defined as the arithmetic average of 6am and 3pm outdoor effective
temperatures), and c) the prescriptive method for use whenever the first two approaches
were not feasible (presented as summer and winter comfort zones on the psychrometric
chart). Acceptable ranges of operative temperature were applied symmetrically above and
below predicted optimum operative temperatures. The average winter prescription for 90%
general thermal acceptability (excluding local discomforts) was given as 22.5°C ± 1.2 K
while the summer prescription was given as 23.5°C ± 1.2 K.
The variable temperature standard for use in naturally ventilated buildings was given as an
adaptive linear regression model based on outdoor weather and climate:
optimum indoor temperature = 18.9 + 0.255 * (outdoor mean ET*)
Acceptable temperature ranges around the optimum in naturally ventilated buildings were
specified as ±3.5 for 80% general acceptability and ±2.5 for 90% general acceptability.
The RP-884 project leads to the conclusion that the PMV model represents a useful adjunct
to comfort standards intended for use exclusively within HVAC buildings where occupants
have little or no opportunity to adapt themselves, nor their immediate occupied zone.
However, application of this same model in naturally ventilated settings leads to significant
errors since it overlooks an important adaptive response in the form of variable thermal
expectations of building occupants in such buildings. In naturally ventilated settings we
recommend the application of an adaptive model that predicts optimum indoor temperature
from a knowledge of the building’s meteorologic or climatic setting.
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ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 1
CHAPTER 1 - INTRODUCTION & BACKGROUND
1.1. Introduction
The way we design, construct, and operate buildings has profound implications for the
quality of both the natural and built environments. All too often today’s buildings require
massive resource inputs, create bleak or potentially unhealthy indoor environments,
pollute both their local and global environments through increased greenhouse
emissions, as well as contributing to the destruction of natural habitats (Barnett and
Browning 1995). The energy required to heat and cool our buildings, and the very way
we define the “comfortable” thermal conditions we are trying to maintain, play significant
roles in this environmental impact. The use of energy for heating, ventilating and air-
conditioning (HVAC) of the indoor environment is already the largest sector in energy
consumption in most of the developed world (Griffiths et al 1988). As well we seeing a
significant increase in HVAC energy use in developing and newly industrialized
countries as well (Ang 1986, Abro 1994). This is particularly relevant to the rapidly
developing tropical regions of the Asia-Pacific region, where traditional lifestyles in
naturally ventilated buildings are giving way to an increased reliance on mechanical
cooling. This in turn is changing both the way we design buildings and building
occupants’ expectations and behavioral patterns related to air conditioning (Lovins
1992).
It is commonly estimated that persons in economically developed countries spend at
least 80% of their time indoors. This suggests that the quality of the indoor environment
can have a significant impact on comfort, health, and overall sense of well-being. In an
effort to maintain the quality of the indoor environment, we mechanically condition our
buildings to provide constant, uniform, “comfortable” environments. The current
standards that define what those “comfortable” conditions should be were conducted
primarily with university students and in mid-latitude climate regions (ASHRAE 1992,
ISO 1994). Other than allowing for only a slight seasonal shift in the comfort zone based
on clothing adjustments, it is often suggested that the standards are universally
applicable across all building types, climates, and populations (Parsons 1994 and
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 2
discussion). A strict reliance on laboratory-based comfort standards also ignores
important cultural and social differences in the need or desire for air conditioning. A
special issue of Energy and Buildings (Kempton and Lutzenhiser 1992) focused on
these non-thermal issues, with a variety of papers examining how individuals and
cultures vary in their perceived need for and expectations of air conditioning.
But perhaps the single biggest issue in this debate remains the applicability of
standards in buildings which aren’t air conditioned at all. For example, when recently
asked by a union official whether or not Standard 55 (ASHRAE 1992) was applicable to
un-air-conditioned premises, ASHRAE’s Technical Committee (TC 2.1) responsible for
the standard openly declared that their comfort charts were intended for both HVAC and
naturally ventilated premises. Many researchers, however, challenge this assumption of
universal applicability, arguing that it ignores important contextual differences that can
attenuate responses to a given set of thermal conditions. While the “comfort zone” might
be viewed by the engineering community as a design goal for a deterministic HVAC
control system, its relevance to naturally ventilated buildings where conditions are
inherently much more variable is questionable (Forwood 1995). This was also
acknowledged by Givoni (1992), who revised his already notable work on the building
bioclimatic chart. He expanded the boundaries of the comfort zone based on the
expected indoor temperatures achievable with different passive design strategies,
applying a “common sense” notion that people living in unconditioned buildings become
accustomed to, and grow to accept higher temperature or humidities. Strict and literal
interpretation of the static “comfort zone” precludes application to anything other than
full-blown HVAC designs across the world’s moderate to extreme climate zones.
An alternative to traditional comfort theory - termed the “adaptive model” of comfort -
embraces the notion that people play an instrumental role in creating their own thermal
preferences. This is achieved either through the way they interact with the environment,
or modify their own behavior, or because contextual factors and past thermal history
change their expectations and thermal preferences. Interest and research into this
“adaptive” theory of thermal comfort first began in the mid-70’s in response to the oil-
shocks, and has recently regained momentum due to increasing concerns over human
impact on global climatic environment. There are numerous benefits to be gained from
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 3
an improved understanding of the influence of adaptation on thermal comfort in the built
environment. These include improved predictive models and standards, more
sophisticated and responsive environmental control algorithms, increased opportunities
for personal control, enhanced levels of thermal comfort and acceptability among
occupants, reduced energy consumption, and the encouragement of climatically
responsive and environmentally responsible building design.
This research project, “ASHRAE RP-884 - Developing an Adaptive Model of Thermal
Comfort and Preference”, is premised on the development and analysis of a quality-
controlled, cumulative database compiled from previous thermal comfort field
experiments worldwide. The aim is to use this database to refine our conceptual
understanding of adaptive mechanisms, to develop an empirical model of the adaptive
process, and to propose a variable temperature standard to supplement the current
ASHRAE Standard 55 (1992).
1.2. Defining the adaptive process
1.2.1. The dialectic of contemporary thermal comfort theory
In contemporary thermal comfort research, there is a perceived irreconcilable split into
“static” and “adaptive” schools of thought (Auliciems 1989; Nicol 1993). In the “static”
camp are ASHRAE’s Standard 55 --Thermal Environmental Conditions for Human
Occupancy (ASHRAE 1992) and the ISO Standard 7730 (ISO 1994). The static model
essentially views the person as a passive recipient of thermal stimuli. It is premised on
the assumption that the effects of a given thermal environment are mediated exclusively
by the physics of heat and mass exchanges at the surface of the body, while the
maintenance of a constant internal body temperature necessitates some physiological
responses. It is generally assumed in the static school of thought that thermal
sensations (hot-warm-cool-cold) are proportional to the magnitude of these
physiological responses, as measured by mean skin temperature and latent heat loss or
wettedness due to sweating (Benzinger 1979). The deterministic logic underpinning
heat balance comfort models such as PMV, ET* and SET* is:
physics ⇒ physiology ⇒ subjective discomfort
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 4
These models are based on extensive and rigorous laboratory experiments, and yield
fairly consistent, reproducible results in climate chambers. However, researchers are
increasingly exploring the extent to which we can directly apply these laboratory-derived
models, without modification, to the task of predicting subjective responses to thermal
conditions in real buildings, where the interactions between the occupants and indoor
climate are exceedingly complex. Adherents to the adaptive school of thought regard
the simplistic cause-and-effect approach embodied in the static models as inadequate
to describe thermal perception in the real world. As such the static hypothesis has
come to be regarded as a “single temperature” model of thermal comfort (Humphreys
1981, 1994a, Nicol 1993: Auliciems 1989). But a more conciliatory interpretation of the
heat balance model depicts it as partially adaptive, since it does include the impact of
thermal variables and clothing which can be adjusted by the occupant.
1.2.2. The “adaptive” hypothesis
With the static heat-balance models representing one side, on the other side of this
dialectic is the “adaptive” school of thought in which factors beyond the fundamental
physics and physiology all interact with thermal perception. These factors can include
demographics (gender, age, economic status), context (building design, building
function, season, climate, semantics, social conditioning), and cognition (attitude,
preference, and expectations) (McIntyre 1982, Baker 1993, Baker and Standeven 1994,
Oseland 1994a,b, Griffiths et al 1988). These factors have been demonstrated time
and again to be irrelevant to the comfort responses of subjects in the contrived setting of
the climate chamber (Fanger 1972b, de Dear et al 1991a). However, there remains a
lingering suspicion in the minds of adaptive modellers and practitioners alike that such
considerations cannot be dismissed so easily in the context of real buildings.
The generic term “adaptation” might broadly be interpreted as the gradual diminution of
the organism’s response to repeated environmental stimulation. As used in RP-884,
adaptation subsumes all physiological mechanisms of acclimatization, plus all
behavioral and psychological processes which building occupants undergo in order to
improve the “fit” of the indoor climate to their personal or collective requirements. Within
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 5
this broad definition it is possible to clearly distinguish three categories of adaptation
(Folk 1974, 1981, Goldsmith 1974, Prosser 1958, Clark and Edholm 1985):
1. Behavioral Adjustment. This includes all modifications a person might consciously,
or unconsciously make, which in turn modify heat and mass fluxes governing the body’s
thermal balance. We define adjustment in terms of three subcategories:
a) Personal adjustment: adjusting to the surroundings by changing personal
variables, such as adjusting clothing, activity, posture, eating/drinking hot/ cold
food or beverages, or moving to a different location;
b) Technological or environmental adjustment: modifying the surroundings
themselves, when control is available, such as opening/closing windows or shades,
turning on fans or heating, blocking air diffusers, or operating other HVAC
controls, etc.; and
c) Cultural adjustments, including scheduling activities, siestas, dress codes
2. Physiological. The most comprehensive definition of physiological adaptation
would include all of the changes in the physiological responses which result from
exposure to thermal environmental factors, and which lead to a gradual diminution in the
strain induced by such exposure. Physiological adaptation can be broken down into at
least two subcategories:
a) Genetic adaptation: alterations which have become part of the genetic
heritage of an individual or group of people, but developing at time
scales beyond that of an individual’s lifetime, and
b) Acclimation or Acclimatization (used interchangeably here): changes in the
settings of the physiological thermoregulation system over a period of days
or weeks, in response to exposure to single or a combination of thermal
environmental stressors.
3. Psychological. The psychological dimension of adaptation to indoor climate refers
to an altered perception of, and reaction to, sensory information. Thermal perceptions
are directly and significantly attenuated by one’s experiences and expectations of the
indoor climate. This form of adaptation involves building occupants’ “comfort setpoints”
which may vary across time and space. Relaxation of indoor climatic expectations can
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 6
be likened to the notion of habituation in psychophysics -- repeated or chronic exposure
to an environmental stressor leading to a diminution of the evoked sensation’s intensity
(Glaser 1966, Frisancho 1981).
habituationpsychological adaptation -
changing expectations
adjustmentbehavioral/technologicalchanges to heat-balance
Adaptation toIndoor Climate
acclimatizationlong-term physiological
adaptation to climate
Figure 1.1: The three components of adaptation to indoor climate
1.3. A conceptual model of adaptation -- feedback loops
An important premise of the adaptive model is that the building occupant is no longer
simply a passive recipient of the thermal environment as given, as in the case of a
climate chamber experimental subject, but instead is an active agent interacting with all
levels of the person-environment system via feedback loops. We continue to
emphasize, however, our opinion that this perspective complements rather than
contradicts the “static” heat-balance view as outlined above. The heat-balance model
does partially account for adaptation by using as inputs those parameters affected by
adjustment and environmental interventions, but it explicitly rules out any notions of
physiological and psychological adaptation.
In contrast, the adaptive model draws upon a phenomenological perspective that
emphasizes how people interact with and change their environment, and accounts for
the ways in which a person’s past experience, future plans, and intentions influence
one’s perception (Canter 1983, Wohlwill 1974, Helson 1964, Veitch and Arkkelin 1995,
Kaplan and Kaplan 1982). The adaptive hypothesis indicates that one’s satisfaction
with an indoor climate is achieved by a correct matching between the actual thermal
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Introduction & Background page MRL Australia 7
environmental conditions prevailing at that point in time and space, and one’s thermal
expectations of what the indoor climate should be like. Thermal expectations result from
a confluence of current and past thermal experiences, cultural and technical practices
(Auliciems 1981, 1989, de Dear 1993, Nicol 1993). These relationships have been
described in Figure 1.2, a schematic diagram developed by Auliciems (1981, 1989)
showing that a given set of indoor climatic conditions can elicit varying levels of comfort
and satisfaction from building occupants, depending on culture or climatic and
HVAC/architectural expectations.
Figure 1.2: The "adaptive model" of thermal perception (after Auliciems, 1981)
By logical extension, the adaptive hypothesis also implies that the temperatures people
expect indoors for comfort and satisfaction will move in the direction of the average
conditions encountered in their day-to-day life, both indoors and out. So, in the systems
schematic in Figure 1.2, outdoor climate acts as a negative feedback which attracts the
thermal perceptual sub-system’s set point, thereby damping load error
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(dissatisfaction/discomfort) within the human behavioral thermoregulatory system. The
net result is that adapted building occupants may be perfectly comfortable at
temperatures beyond those recommended in standards such as ASHRAE 55 (1992)
and ISO 7730 (1984, 1994).
We believe that the development of an adaptive predictive model of thermal comfort
should combine features of both the static and adaptive theories, and that these various
feedback loops should be described in terms of how they affect the more traditional
linear relationships. As set out in the heat balance models
(physics ⇒ physiology ⇒ subjective discomfort)
1.3.1. Behavioral feedback - adjustment
Behavioral adjustment of the body’s heat-balance probably offers the greatest
opportunity for people to play an active role in maintaining their own comfort. The extent
to which building occupants can, or do, behaviorally interact with their indoor climate
depends a great deal on contextual factors. This is very important in both the
development and application of an adaptive model, and deserves further elaboration.
Context can be described in terms of adaptive opportunity, compared to the constraints
or restrictions on thermoregulatory degrees of freedom (Nicol and Humphreys 1972).
That is, “adaptive opportunity” refers to whether or not buildings afford their occupants
scope for adaptive interventions (Baker and Standeven 1994). This may result from:
a) an attribute of the building itself (e.g. are windows operable? how far are
occupants placed away from such windows? is the floor plan individual office
cells or open-plan bureau landschaft?),
b) characteristics of the active, or energy consuming, climate services inside
the structure (e.g. centralized HVAC services, or decentralized task
conditioning controls at each workstation?), or
c) the organizational and social conditions prevailing within the building (e.g. is
there a strict or casual dress code? are employees bound to a single
workstation for the entire working day?).
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The flip-side of adaptive opportunity (i.e, the lack of...), is the analysis of constraints to
thermal control. These constraints may be gathered under five main headings (Nicol
and Humphreys 1972, Humphreys 1994a):
a) Constraints due to climate. Buildings in harsh or extreme climates might
present a more exclusive barrier to the elements than buildings in milder
climate, affording their occupants fewer adaptive opportunities.
b) Economic constraints. The costs of thermal environmental control, both
initial and recurrent, often exceed the resources of many countries.
c) Constraints due to social custom or regulation. To what extent can an
individual change his/her clothing? Are clothing patterns determined by
climate, fashion or religion? To what extent do the various requirements put
on us by other people, government energy guidelines, greenhouse gas
emission quotas or targets limit our freedom to behaviorally thermoregulate?
d) Constraints due to task or occupation. Often the requirements of a particular
job override those of thermal comfort, when there are formal dress codes of
fixed work locations.
e) Constraints due to design. This refers to design of the building or HVAC
system, availability of task-conditioning or personal environmental controls,
design quality of awnings, climatic suitability of window placement and size.
The concept of adaptive opportunity helps to differentiate those buildings in which a
deterministic relationship between the thermal environment and human response is
applicable, and those in which an adaptive feedback loop is fully operational. Adaptive
opportunity can be thought of as a continuum. At one extreme is the climate chamber in
which subjects are instructed what to wear and what activities they are to perform while
an external agent, the researcher, determines the temperature, humidity and air flow
regime they are to experience for the duration of the experiment. At the other extreme
we find the single-occupant room in which clothing and activity patterns are discretionary
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 10
and environmental controls cover the full range of possibilities from operable windows
through to task-ambient air conditioning.
The ultimate efficacy of any form of adaptive control must be measured in terms of
occupant satisfaction and ideally should be evaluated in terms of available control
(adaptive opportunity) vs. exercised control (actual physical control that takes place) vs.
perceived control (Paciuk 1989, 1990). But regardless of whether it is placebo or real
control, there seems little dispute in the literature that the issue of personal and
environmental control is central to thermal acceptability, and therefore should be a factor
examined in the RP-884 data analysis.
Behavioral adjustment represents the most immediate feedback link to the thermal
environment. Stated simply, if a person is uncomfortable, or expects to become so, they
are to take corrective action. What might have previously been regarded as the final
consequence in the static heat balance model (the conscious sensation of thermal
discomfort), becomes the starting point for this feedback in the adaptive model.
indoor clothing body’s physiol. thermal discomfort climate + activity heat load regulation sensation dissatisfaction Behavioral Adjustment
Figure 1.3: Behavioral feedback loop
1.3.2. Physiological Feedback -- acclimatization
Physiological acclimatization to cold stress is primarily associated with maintenance of
warmer skin temperatures and increased heat production, although it is not clear to what
extent the increased metabolic rate can occur without shivering (Frisancho, 1981).
Otherwise, adaptation to the cold is primarily behavioral (Clark and Edholm 1985). The
evidence for physiological acclimatization is more thoroughly documented for heat
exposure, be it metabolically or environmentally induced (Folk 1974, 1981, Fox 1974,
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Introduction & Background page MRL Australia 11
Bruce 1960, Berglund and McNall 1973, Givoni and Goldman 1973). The primary
physiological response to prolonged heat stress induced by a regime of work in heat is
an increased sweating capacity for a given heat load. Other changes related to
thermoregulatory sweating include a fall in the setpoint body temperature at which
sweating begins, triggering the onset of sweating earlier. A heat acclimatized person
also achieves a better distribution of sweat over their skin compared to an
unacclimatized person under the same heat load. Faced with comparable levels of heat
challenge, the heat acclimatized person also demonstrates a variety of cardiovascular
responses such as reduced heart rate, an increased blood volume and peripheral blood
flow (Fox 1974, Bean and Eichna 1943, Hardy 1961, Wyndham 1970). Acclimatization
to heat takes place mainly in the first week of exposure, while a longer period is required
for cold acclimatization or for resting or sedentary activity (Bruce 1960).
This picture of acclimatization can be regarded as most appropriate to hot-dry climate
zones. The pattern in hot-humid climates, however, differs significantly (Gonzalez et al
1974, Goldman et al 1965). In particular, the elevated capacity for sweating observed in
hot-dry situations seems to be less important in the humid condition due to the reduced
evaporative potential of the environment. Thus, while sweat secretion in the humid
acclimatized subject is initiated at a core temperature lower than that for the
unacclimatized subject, the shortfall in body heat dissipation in the humid condition
appears to be taken up by increased dry heat losses from the skin which result from an
increased peripheral blood flow and skin temperature.
Acclimatization is an unconscious feedback loop mediated by the autonomic nervous
system, that directly affects our physiological thermoregulation setpoints. Like
behavioral adjustment depicted earlier, the physiological feedback process of
acclimatization can also be depicted schematically:
outdoor indoor physiol. strain discomfort & climate climate & regulation dissatisfaction Acclimatization
Figure 1.4: Physiological feedback loop
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1.3.3 Psychological feedback -- habituation and expectation
Psychological adaptation encompasses the effects of cognitive and cultural variables,
and describes the extent to which habituation and expectation alter thermal perceptions.
This concept has been most clearly elaborated under the banner “adaptation-level
theory” (A-LT). A-LT introduces the notion of optimal levels of stimulation, or adaptation
levels, along with a view of environmental stress resulting from excessive deviations
from such optimal levels. These optimal adaptation levels result from past exposure,
and act as benchmarks for environmental evaluations (Wohlwill 1974, Helson 1964).
Studies of the general nature of perception and its relationship to environmental stimuli,
memory and cognition, and contextual factors such as building type or season, can also
offer insights into understanding thermal comfort in buildings (de Dear et al 1991c,
Helson 1971, Ittelson 1973, Auliciems 1981, Russell and Ward 1982).
The role of expectation in thermal comfort research was acknowledged in the earlier
work of McIntyre (1980), who stated that “a person’s reaction to a temperature which is
less than perfect will depend very much on his expectations, personality, and what else
he is doing at the time.” Although the least studied of the three adaptive mechanisms,
psychological adaptation might actually play the most significant role in explaining the
differences between observed and predicted thermal responses. This applies
particularly in light of different environmental contexts such as the laboratory vs. home vs.
office, or when comparing responses in air-conditioned vs. naturally-ventilated buildings
(Fishman and Pimbert 1982, Heijs and Stringer 1988, Bush 1990, de Dear et al 1991c,
Rowe et al 1995, Oseland 1995,).
In terms of a feedback loop that can be incorporated into our conceptual model of
adaptation, expectation and habituation are influences by one’s current thermal
experience or one’s longer history of experiences with both the indoor and outdoor
climate. This in turn directly affects our thermal sensation and cognitive assessments of
thermal acceptability as described in Figure 1.5.
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outdoor indoor physiol. strain thermal discomfort climate climate (Tsk, wet) sensation dissatisfaction Climatocultural practices & norms, Expectation HVAC & architecture & Habituation
Figure 1.5: Psychological feedback loop
1.4. Literature review
The relevant literature for this project is classified into two broad categories: 1)
climate chamber evidence for adaptation to climate, and 2) field evidence for
adaptation. Within the second category, we review some of the earliest studies of
adaptation, as well as an analysis of more recent, rigorously conducted field studies in
both air-conditioned and naturally ventilated buildings. The literature review of field
studies will be further sub-classified in terms of specific evidence for both behavioral
and psychological adaptation
1.4.1. Climate chamber evidence for adaptation to climate
A research design for experiments known as the “preferred temperature method” has
been applied by various researchers over the years to the questions raised by the
adaptive hypothesis. This method is very suitable for testing the adaptive feedback in a
laboratory setting because the environmental temperature within the chamber is directly
controlled by its single occupant, the subject. What follows is a summary of some of the
more pertinent results.
Fanger et al (1977) investigated the effects of differing climatic experiences, and by
implication, adaptive states, on thermal comfort responses by comparing the
temperature preferences of climatically disparate samples. In one study, sixteen Danish
subjects wore a standard 0.6 clo ensemble and sat quietly in a string chair (assumed to
exert negligible effect on their clothing insulation), one-at-a-time in a climate chamber for
2.5 hr. Subjects were selected for the study because of their regular swimming in the
ocean off Copenhagen during winter (lat. 56°N, mean February air temperature 0°C).
The sample was found to have the same preferred temperature, about 25.5°C, as
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Introduction & Background page MRL Australia 14
regular Danish college students (not winter swimmers) under the same experimental
conditions (Fanger and Langkilde 1975). Another Danish sample with cold exposures
consisted of 16 meat-packers from a refrigerated storeroom (Fanger et al 1977). They
too had the same preferred temperatures as the winter swimmers and college students.
If cold exposure fails to influence temperature preference, the next question is whether or
not heat exposure has an effect. As noted earlier, physiologists have a clearer picture of
heat, as opposed to cold, acclimatization, and much of that work refers specifically to
heat stress conditions of the type induced by a regime of work in heat. Very little
research has been done into the effects of acclimatization on thermal discomfort in the
moderate heat stress range. In one such study, Fanger (1972a) recruited a sample of
16 long-term inhabitants of the tropics shortly after their arrival in Copenhagen. The
same procedure as described above was followed, and the result, again, was that
temperature preferences were not significantly different.
Acknowledging the limited “shelf-life” of physiological heat acclimatization, de Dear et al
(1991b) replicated Fanger’s tropical experiment on location in Singapore (lat. 1°N)
using a sample of 32 college students. Attention to detail in the replication went as far
as borrowing the standard 0.6 clo KSU uniforms from Fanger's Danish laboratory, and a
chair similar to the Danish string chair was also used. Again, temperature preferences
turned out not to be significantly different from those of Fanger's benchmark Danish
subjects ~ circa 25.5°C (de Dear et al 1991b).
Gonzalez (1979) studied the role of natural heat acclimatization (humid) during a five day
heat wave in New Haven Connecticut during which day-time temperature maxima
ranged between 32°C to 37°C and 88% to 90% rh. Twenty young male subjects
participated. For lightly exercising subjects (116 W m-2), there was a discernible
increase in preferred temperature (as assessed by a rating scale) after the heat wave
(Gonzalez 1979). However, there were no statistically significant differences in thermal
comfort or acceptability responses of resting subjects between the before-and-after
heat wave tests.
The only significant departure from this picture of overall consistency in chamber
research results has been a recent, but as yet unpublished, PhD thesis from the
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University of London (Abdulshukor, 1993). Three results from that study have been cited
by Humphreys (1994a):
• Chinese subjects in a Malaysian climate chamber preferred a temperature of
28.0°C,
• Malay subjects in a Malaysian climate chamber preferred an even warmer
temperature at 28.7°C, while
• Malay subjects in a London climate chamber study preferred only 25.7°C.
A clear implication of these results is that the hot and humid climatic context of the Malay
peninsular was responsible for a three degree elevation of temperature preferences.
These Malaysian climate chamber results are perplexing insofar as the same ethnic
groups (Chinese and Malays) with exactly the same thermal histories and experiences
(Singapore lies at the tip of the Malay peninsula) were represented in the de Dear et al.
(1991b) chamber study. Using exactly the same clothing, metabolic rate and
experimental protocol as used in Fanger’s Danish studies, the temperature preferences
in Singapore’s climate chamber were three degrees cooler than these unpublished
Malaysian results.
In conclusion, on the basis of the majority of experimental evidence published to date,
subjective discomfort and thermal acceptability under conditions most typically
encountered in residences and office buildings, by resting or lightly active building
occupants, appear to be unaffected by the physiological processes of acclimatization.
1.4.2. Field evidence for adaptation
While chamber studies have the advantage of testing under carefully controlled
conditions, field studies are best used for assessing the potential impacts of behavioral
or psychological adaptations as they occur in realistic settings. If people feel thermally
comfortable in conditions that fall outside of the ASHRAE comfort zone, it seems likely
that adaptation has played a role. While the majority of published field studies collected
the necessary data to determine whether people are comfortable when conditions are in
or out of the comfort zone, only a subset of the data contains sufficient detail to
disentangle the causal mechanisms behind those responses. In other words, exactly
what kind of adaptation was taking place?
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Introduction & Background page MRL Australia 16
1.4.2.1. The earlier field evidence for adaptation
Subjective assessments of thermal comfort typically use the rating scale method
(McIntyre 1978), where comfort is operationalized as a vote coinciding with the central
category of a thermal sensation, or comfort scale (“neutral”, or “comfortable”). The
ambient temperature found by statistical analysis to most frequently coincide with this
central rating is referred to as the sample's "neutrality" and is denoted here as Tn. The
typical cross-sectional field study consists of a questionnaire with rating scales
administered to building occupants while simultaneously recording indoor climatic
variables. The most important of which is air temperature. The simplest of these
studies are based on single-point measurements of temperature, and possible humidity.
Numerous such studies have been published over the years, and Humphreys' (1975)
review of 36 examples from various countries around the world uncovered a strong
statistical dependence of thermal neutralities (Tn) on the mean levels of air or globe
temperature (Ti) recorded within the buildings:
Tn = 2.56 + 0.83 Ti (r=+0.96) eq.1.1
It was noted that building occupants were able to find comfort, assumed to be a vote on
the central category of rating scales, in indoor temperatures spanning more than 13 K.
Humphreys (1975) attributed this to the adaptive processes, concluding that "... the
range of recent experience is better regarded as one of the factors which will contribute
to the acceptability of the environment to which the respondent is exposed."
Reasoning that indoor temperatures are dependent on outdoor temperatures to varying
extents, Auliciems suggested that there might be a statistical relationship between
indoor thermal neutralities and outdoor climate as well (Auliciems 1969).
Parameterizing “outdoor climate” as mean monthly temperature (i.e. average of the
average daily minima and average daily maximum for the month in question),
Humphreys (1978) followed up Auliciems’ suggestion and found convincing evidence for
adaptation to outdoor climate, as depicted in Figure 1.6. The influence of external
climate on indoor neutralities is particularly evident in the results from the so called "free
running" buildings which had neither centralized heating nor cooling plant (naturally
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 17
ventilated). In such buildings, the following linear regression model accounted for 94%
of the variance in neutralities:
Tn = 11.9 + 0.534 Tm (r=+0.97) eq.1.2
Climate controlled (centralized HVAC) buildings, on the other hand, had a less
pronounced but still highly significant correlation with outdoor mean monthly temperature
(Tm), but with a curve rather than a straight line achieving the best fit:
Tn = 23.9+.295(Tm-22) * exp(-((Tm-22)/(24*√2))2) (r=+0.72) eq.1.3
Auliciems (1981) subsequently revised Humphreys’ regression database by deleting
incompatible field studies, such as those based on asymmetric rating scales or children
as subjects, and adding more recent studies that had been published after Humphreys’
(1976) paper. These revisions brought the database up to 53 separate field studies
from various climatic zones in Australia, Asia, the Americas and Europe. After
collapsing free running and climate controlled buildings together, the resulting equation
was:
Tn = 0.48 Ti + 0.14 Tm + 9.22 (r=0.95) eq.1.4
where r is the multiple correlation coefficient. Even though the regression coefficients
may be unstable in such a model due to intercorrelation between the two independent
variables, equation 1.4 represents a widely cited statistical expression for the adaptive
hypothesis of human thermal perception.
While the statistical association between neutralities and prevailing outdoor climate
appears quite strong and convincing in Figure 1.6, the actual causal mechanism is left
in doubt by such “black box” adaptive models. Apart from thermal habituation and
acclimatization, there are several other plausible hypotheses, including the possibility
that some unmeasured variables in the human body's heat balance were compensating
for environmental temperature. For example, adjustments such as reduced clothing,
metabolic and humidity levels may combine with higher air velocities in the warm climate
studies (to the right-hand side of Figure 1.6) to cause subjects to experience thermal
neutrality at considerably higher indoor temperatures than would otherwise have been
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 18
the case. Therefore, to more rigorously test the physiological and psychological bases
of the adaptive hypothesis, these behavioral alternatives need to be eliminated, or at
least accounted for. More recent field studies and experiments have done just that, by
collecting simultaneous measurements of all of the input variables to Fanger’s PMV
model (ISO, 1994). Such studies allow a closer look at the causal mechanisms driving
thermal adaptation indoors.
FIGURE 1.6: The statistical dependence of indoor thermal neutralities on climate
(after Humphreys, 1976)
1.4.2.2. Analysis of neutral temperatures using recent field experiments
de Dear’s Ph.D. thesis, entitled “Perceptual and Adaptational Bases for the
Management of Indoor Climate - A Study of Warm Climates” (1985) and subsequent
ASHRAE Transactions paper (de Dear and Auliciems, 1985) reported on six thermal
comfort experiments in office buildings scattered across various Australian climatic
zones, ranging from equatorial (Darwin) through sub-tropical (Brisbane) to mid-latitude
14
16
18
20
22
24
26
28
30
32
-6 -1 4 9 14 19 24 29 34mean monthly outdoor temperature (C)
ind
oo
r n
eutr
ality
(C
)
climate controlled buildings
free running buildings
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 19
(Melbourne). The research design was premised on a consistent field method across
the various climatic and building types, including instrumentation, questionnaire,
protocols and analysis, thereby permitting climatic and contextual effects to be
disentangled from the dozens of methodological artefacts that potentially confound
earlier investigations. In both Melbourne and Brisbane, two experiments were
conducted during their respective summer seasons, one in free running buildings and
the other in climate controlled buildings. In total, these Australian samples included over
1100 office building occupants who cast questionnaire assessments of indoor climatic
environments on 3290 separate occasions. Figure 1.7 contains the neutralities,
estimated by probit analysis, for the Bedford scale in each of the six experiments,
plotted against the corresponding mean monthly outdoor temperatures. Neutralities
tend to increase from Melbourne's mild summer through to equatorial Darwin. This trend
is most pronounced in the free running (FR) buildings (codes F for Brisbane, C for
Melbourne). The Brisbane sample had the warmest neutrality in Australia at 25.6°C,
while Melbourne's FR sample had the coolest at 21.8°C. The climate controlled
buildings in Australia on the other hand all had neutralities clustered within the 23-24°C
range.
Apart from the neutralities observed in the six Australian field experiments, neutralities
predicted by Auliciems' thermal adaptive model (eq.1.4) are also shown in Figure 1.7,
as are the predictions based on the PMV heat-balance model. It should be noted that
these PMV predictions differ from those presented in the original publication (de Dear
and Auliciems, 1985). Average clo values observed in the experiments have since
been increased by 0.15 clo units to account for the insulation value of a typical office
chair (Schiller 1990, Fanger and Wyon 1990, McCullough and Olesen 1994). This
having the net effect of lowering the PMV model's neutrality predictions by over a full
degree, which in turn halves the average prediction error down to 0.7°C (absolute value).
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Introduction & Background page MRL Australia 20
18
20
22
24
26
28
30
-8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
mean outdoor temperature (oC)
ind
oo
r n
eutr
ality
(oC
)
Observedneutrality
Adaptivemodel
Static PMVmodelP
A O
B
M
C
DE
F
G N
H
I
J
K LClimate-controlled
Free-running
Figure 1.7: Thermal comfort experiments in the field: Observed and predicted neutralities in relation to outdoor climate
Also depicted in Figure 1.7 are some results from six ASHRAE-sponsored Class I field
experiments in climate-controlled buildings across a variety of climatic contexts. Two
experiments are from San Francisco (Schiller et al 1988a; Schiller 1990). Another two
Code Location & season Climate-controlled or Free Running
Author
P Montreal-Winter CC Donnini et al (1996) A San Francisco-winter CC Schiller et al (1988a) O Montreal-Summer CC Donnini et al (1996) B San Francisco-summer CC Schiller et al (1988a) M Townsville-Dry CC de Dear + Fountain (1994) C Melbourne-summer FR de Dear + Auliciems (1985) D Melbourne-summer CC de Dear + Auliciems (1985) E Brisbane-summer CC de Dear + Auliciems (1985) F Brisbane-summer FR de Dear + Auliciems (1985) G Darwin - Dry CC de Dear + Auliciems (1985) N Townsville-Wet CC de Dear + Fountain (1994) H Singapore FR de Dear et al (1991) I Singapore CC de Dear et al (1991) J Bangkok FR Busch (1990) K Darwin-Wet CC de Dear + Auliciems (1985) L Bangkok CC Busch (1990)
ASHRAE RP-884 Final Report
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are from tropical Townsville (de Dear and Fountain 1994), and another pair from
Montreal (Donnini et al, 1996).
Plotted along with the San Francisco observed neutralities are some predictions from
Auliciems' (1983) adaptive model as well as Fanger's PMV (heat balance) model, after
the effect of chair insulation (0.15 clo) has been added to Schiller et al's published
clothing insulation estimates. Clearly in both seasons, the adaptive model comes very
close to observation, but so too does the static heat balance model. This general
pattern of consistency between neutralities observed in air-conditioned buildings and
PMV predictions also extends to the more recent ASHRAE-sponsored studies in office
buildings located in a hot-humid climate (de Dear and Fountain 1994) and cold climate
(Donnini et al, 1996).
Busch's (1990) field experiments in office buildings in tropical Bangkok have also been
included in Figure 1.7. Both climate controlled (air-conditioned) and free running
buildings were studied, so a diverse range of thermal environments was covered by the
sample size of 1146. For the climate controlled buildings, neutrality was established at
24.5°C (code L in Figure 1.7), within a degree of the PMV prediction based on Busch's
mean clo value of 0.56 plus some chair insulation (0.15 clo). In Bangkok's free running
buildings, Busch observed a neutrality of 28.5°C (code J in Figure 1.7), which appears
to be over three degrees (K) warmer than predicted by Fanger's PMV. Auliciems'
(1983) adaptive model, on the other hand, came within half a degree of the observed
result. Busch suggested that the lighter clothing and higher local wind explain most of
the disparity between observed thermal neutralities in the naturally ventilated and air-
conditioned buildings, implying that behavioral adjustments were playing a strong
adaptive role. But there are clearly other factors at play, as well. Noting that clothing
and air velocity are used as input parameters to the heat balance models, the fact that
PMV still underestimates neutrality suggests that occupants were influenced by other
modes of adaptation unaccounted for by the heat balance inputs. In particular, PMV’s
underestimation of thermal neutrality more significantly in the free running building
sample than in the climate controlled building sample suggests that context and
adaptive opportunity can influence expectations and thermal response to the indoor
environment.
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Another example of this is found in a more recent field experiment, in which de Dear et
al (1991c) examined climate controlled office buildings and free running residential
apartment blocks in equatorial Singapore. As seen in Figure 1.7 (code I), the observed
neutrality of 24.2°C in the air conditioned buildings was accurately predicted by both the
adaptive and heat balance models after 0.15 clo chair insulation was added to clothing
estimates. But as with Busch's Bangkok experiment, the 28.5°C neutrality observed in
Singapore's naturally ventilated apartment buildings (code H) was most closely
approximated by the adaptive model with a prediction of 27.2°C.
1.4.2.3. Evidence for behavioral adaptation - personal/environmental adjustment
There have been a few studies that examined direct evidence of exercised control, or
adjustment. One of the earlier studies that looked closely at clothing patterns was by
Fishman and Pimbert (1982), who studied 26 subjects in a UK office building for an
entire year. The estimated clo values of the Watson House sample had a strong linear
dependence on outdoor weather and season, especially in the case of women subjects,
with a regression gradient of -0.02 clo units per degree of outdoor mean weekly
temperature. This supports the hypothesis that the statistical dependence of indoor
neutrality on outdoor climate may, in part, be due to behavioral adjustments that directly
affect the heat balance, rather than acclimatization or habituation.
This hypothesis is also supported by the work of Humphreys (1994b) and Nicol et al
(1994), in which a study of naturally ventilated buildings in North West Pakistan
concluded that the office workers were comfortable across a wide range of seasonal
temperatures (neutralities varying between 15.7°C in winter, and 26.4°C in summer).
They also concluded that 1~B of the seasonal changes in comfort temperature could
be attributed to the flexibility in the traditional Pakistani clothing worn.
Personal behavioral adjustments over time were looked at in an exploratory study by
Nicol and Raja (1996) in the UK. They found that clothing changes were more strongly
dependent on the succession of outdoor temperatures that occurred prior to the
measurement, compared to the instantaneous or daily mean outdoor temperature, or the
instantaneous indoor temperature. This suggests the importance of time-series
measurements in future field studies designed to evaluate the effect of behavioral
ASHRAE RP-884 Final Report
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adaptation on thermal comfort. Posture is another example of behavioral adaptation,
and they found a correlation with temperature such that posture would change to
increase the effective body surface area available for dry and latent heat exchange as it
got warmer.
In addition to adjusting to the environment, one can directly manipulate the environment
itself. Baker and Standeven (1994) used hourly questionnaires to ask whether subjects
had made adjustments to their clothing or to furniture, doors, windows, shades, fans or
any other part of the building to improve their comfort. Results indicated extensive
occupant-environment interaction - for 23 subjects in 7 buildings, over a total of 864
hours - there were a total of 273 adjustments to controls or other environmental aspects
of the room, and 62 adjustments to clothing.
The extent to which adjustments actually improve thermal comfort is as important as the
frequency with which they’re made. Benton and Brager (1994) conducted a field
experiment of thermal comfort in a centrally-conditioned office building in California,
before and after energy-efficiency retrofit measures were installed. Adaptive opportunity
was addressed by a series of questions on the availability, use, and effectiveness of
coping mechanisms that either altered the physical environment or personal variables.
While modification mechanisms were infrequently cited, when exercised, they
consistently received high ratings for effectiveness. Behavioral mechanisms received
the highest number of citations, and clothing adjustments in particular were given a
relatively high effectiveness rating.
1.4.2.4. Evidence for psychological adaptation - expectation and context
While there is limited field data providing direct evidence for the effects of psychological
adaptation on thermal comfort, the previous analysis of Figure 1.7 suggests that it can
be implied through comparing comfort responses in different contexts. Paciuk (1990)
provided a more direct analysis of the distinction between available control (adaptive
opportunity), exercised control (behavioral adjustment) and perceived control (related to
the psychological dimension and expectation). She found that, in addition to the
traditional list of thermal inputs to the heat balance models, perceived degree of control
was one of the strongest predictors of thermal comfort in office buildings, and had a
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 24
significant impact in shaping both thermal comfort and satisfaction. This finding was
also supported by the work of Williams (1995), in her study in office buildings in the
Northwest of England. The subjects in this study expressed higher levels of satisfaction
when they perceived themselves to have more control over their environment.
Increasing levels of both perceived and available control have implications for the
design of buildings, including their mechanical systems and interior layouts. A good
example is shown in Figure 1.8, which comes from the English researchers Leaman and
Bordass (1993). They administered a standardized indoor environmental quality
questionnaire to thousands of office workers across the UK, and found a strong negative
relationship between perceived control and occupant density in the workplace.
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1 2-4 5-9 10-29 30+
Number of Occupants in Office
Per
ceiv
ed C
on
tro
l R
atin
g
ventilation
heating
Figure 1.8: Relationship between the number of workers sharing an office and perceived level of control over room heating and ventilation systems. (Leaman & Bordass 1993).
This relationship also has implications for air-conditioned vs. naturally-ventilated
buildings. Naturally ventilated buildings typically consist of small offices with single
occupants or small groups of who are usually within reach of an operable window. This
is clearly not the case in most modern air-conditioned office buildings which are
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 25
characterized by deep-space or open-plan floor layouts with dozens if not hundreds of
employees being required to share the same space. The effects of this may be evident
in Figure 1.7, where the naturally ventilated buildings had thermal neutralities
significantly different from the predictions of heat-balance (static) models such as PMV.
These same buildings probably had occupants who perceived a higher degree of
personal environmental control by comparison to their counterparts in centrally air-
conditioned office buildings. The poor predictive capabilities of PMV in naturally
ventilated buildings suggests that adaptive processes other than behavioral adjustment
(which would be accounted for in the heat balance models) must be occurring.
Expectation seems the most likely explanation, since expectation has all but been
eliminated by the climate-chamber method of comfort research. Within the adaptive
hypothesis, such buildings would be expected by their occupants to provide variable
indoor temperatures, and therefore be judged less critically than centrally air-conditioned
buildings. The RP-884 data analysis will pay careful attention to the distinction between
thermal perception in air-conditioned vs. naturally ventilated buildings.
Although naturally ventilated buildings might generally offer a higher level of adaptive
opportunity than air-conditioned buildings, they could still differ in the actual degree of
occupant control they offer. Rowe (1995a) looked at studies in 1) air conditioned
buildings, 2) naturally ventilated buildings, and 3) naturally ventilated buildings with
supplementary on-demand cooling and heating equipment. He found a significantly
higher level of satisfaction in the naturally ventilated buildings with additional
supplementary control. This led to the conclusion that people have a wider tolerance of
variations in indoor thermal conditions if they can exert some control over them, and that
a considerably higher level of satisfaction will be reached if occupants have means of
controlling the upper and lower temperature limits. In Fishman and Pimberts’ (1982)
year-long study in a UK office building, seven of the 26 subjects worked in air-
conditioned areas. The rest were in naturally ventilated offices. While the sample size
was not large, there was still a difference in the thermal responses of these two groups
as temperatures rose above 24°C. People in the air-conditioned offices began voting
much higher on the thermal sensation scale than their colleagues in the naturally
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 26
ventilated work areas, suggesting that they were less tolerant of higher temperatures
and expected homogeneity in their thermal environment.
Several other researchers support this hypothesis regarding occupant expectations and
their effects on thermal perception. In a study conducted by Black and Milroy (1966) in
both air-conditioned and non-air-conditioned office buildings in London, occupants in
the air-conditioned buildings expressed more complaints about temperature
fluctuations, even though the free running building experienced much greater variability.
The occupants were basing their evaluations on the benchmark of their own
preconceptions of what air-conditioning should achieve, rather than on what it actually
provided. In effect, this suggests that increasing levels of sophistication in
environmental control systems and building services are on a treadmill of attempting to
satisfy ever-increasing occupant expectations (de Dear and Auliciems 1986). Another
study by Rohles et al (1977) found that Michigan subjects were more tolerant of high
indoor summer temperatures (32°C ET*) than Texan subjects. Since other heat balance
variables such as clothing or activity could not account for the difference, it was
speculated that the Texans took summer air-conditioning for granted and came to
expect or even demand cool temperatures, therefore becoming more critical of warmer
indoor conditions than their northern counterparts.
1.5. Implications for RP-884
1.5.1. Lessons from static heat balance models
We believe that the split between “adaptive” and “static” heat balance models, or
schools of thought, is not as irreconcilable as the protagonists have suggested. As
mentioned previously, the terms "static” and “constancy" have given rise to a mistaken
idea that models such as PMV and 2-node, plus the thermal comfort standards based
on them, prescribe a single, constant temperature for thermal comfort the world over.
But the PMV and 2-node models do, in fact, predict comfort temperatures moving in the
direction of prevailing outdoor climate -- as seen in the offset of winter and summer
comfort zones in the last few revisions to ASHRAE’s Standard 55. So the static model
of comfort is in reality an “adaptive” model in its own right -- the fundamental distinction
between the static and adaptive models is their underlying basis or postulated cause for
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 27
the shift in comfort temperatures. The former permits only behavioral adjustments
(personal/technological) to heat balance variables such as clothing or air velocity,
whereas the original adaptive models were premised on changing physiological (i.e.
acclimatization) and psychological (i.e. expectations/habituation) setpoints. While this
may seem to be a fine distinction, failure to appreciate it has, in the opinion of the
authors, been responsible for unnecessary controversy between the two sides of this
debate. An important contribution of the RP-884 adaptive model will be to go beyond
the “black-box” approaches of the earlier adaptive models, so that we can better
understanding the underlying processes of adaptive comfort.
Understanding the challenges of applying laboratory-based static models in the field can
provide guidance on issues to consider when developing a new adaptive model that
combines the best of both static and adaptive theories of thermal comfort. One place to
start is to learn from some of the explanations that have been offered for the
discrepancies between predicted and observed thermal sensations in real buildings:
1. Estimating insulation of clothing garments or ensembles. Brager et al. (1994) have
demonstrated the significance of the clothing insulation estimation method on the
actual clo value obtained. The ensemble insulation value differs by as much as 20%
depending on whether one uses the tables and algorithms in the older or newer
versions of ASHRAE Standard 55 (1981, 1992), or ISO 7730 (1994). It will therefore
be important that rigorous statistical correction factors are used to create consistent
ensemble clo values across the RP-884 database.
2. Accounting for the chair insulation. The tendency for PMV to overestimate thermal
neutralities has been reported in several field studies (Schiller 1990), prompting
Fanger and Wyon (1990) to suggest that the method of estimating clothing insulation
might be systematically flawed by omission of the thermal effect that chairs have on
their occupants. McCullough and Olesen (1994) responded by examining the effects
of upholstered office furniture on the total thermal insulation of a heated manikin, and
found that a typical office chair adds approximately 0.15 clo to the value that one gets
by simple addition of individual garment values, as described in ASHRAE Standard
55-92 (ASHRAE 1992) or ISO-7730 (ISO 1994). Even if the original researchers
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 28
supplying raw data to the RP-884 database omitted the effect of chair insulation, it
will be included as part of the RP-884 analysis.
3. Non-uniformities of physical measurements. If field studies take spot-
measurements of general ambient thermal parameters that are separated from the
occupant’s location in space and/or time, then they might not be representative of
what the occupant is actually experiencing at all (Baker 1993). This becomes
particularly important in rooms with transient or spatially non-uniform thermal
conditions, which are more likely to be the case in passive, or naturally ventilated
buildings, or any situations where workers have high levels of personal or
environmental control available to them. An analysis of adaptive comfort would best
be served by using data taken close to the occupant’s location, and at the same time
as the thermal questionnaire. This will be carefully considered when selecting data
for inclusion in the RP-884 database.
4. Behavioral adjustments and perceived control. People adapt to the environment by
adjusting their clothing or activity, modifying their posture or moving to another part of
the room, opening/closing windows, operating fans or other environmental controls.
But why would this cause a discrepancy between the observed and predicted
conditions? In theory, static heat balance models account for clothing, activity, and
thermal environmental parameters, and should therefore, be able to factor the
consequences of the behavioral adjustments into their equations. Probably the most
likely impact of thermal adjustments is the perception of control -- psychologists are
quick to point out that adverse or noxious stimuli are less irritating if the subject
perceives she/he has control over them (Paciuk 1990, Veitch and Arkkelin 1995,
Kaplan and Kaplan 1982). Issues of behavioral adjustment and perceived control will
be given a high priority in the RP-884 analysis, as this represents a potentially
significant feedback loop between discomfort and purposive behavioral
thermoregulation.
5. Thermal sensation, preference, and acceptability. Existing thermal comfort
standards provide guidelines for “thermal acceptability”, while the static heat balance
models on which they’re based only predict “thermal sensation”. As a result, the
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 29
traditional approach has been to indirectly associate specific thermal sensations with
“acceptability”, and to assume that thermal “preference” is synonymous with thermal
“neutrality”. RP-884 will strive to include field experiments in its database that directly
asked about sensation, acceptability and preference, so these assumptions can be
tested.
1.5.2. Time scales of thermal adaptation
Since each class of adaptive response depends on repeated exposure to a given
regime of thermal conditions, the questions of duration of exposure and lag in response
seem relevant to adjustment, acclimatization and habituation adaptive processes. A
review of the literature in this area will reveal, in part, which mechanisms are likely to
play the most significant role in thermal response to the indoor environment and,
therefore, which should receive the greatest attention in the RP-884 analysis.
The significance of the temporal dimension of thermal adaptation is realized when one
considers applications of adaptive models to control algorithms for HVAC systems.
Auliciems was the first to propose such an adaptive algorithm (Auliciems 1986) which
he referred to as a thermobile (as opposed to a thermostat). It was premised on the
adaptive model described in equation 1.4. The question of how long the averaging
period for the algorithm’s temperature inputs should be was left open but, as an initial
guess, Auliciems proposed that the running means, one for both indoor and outdoor
temperatures, should comprise hourly observations across the preceding fortnight.
More recently, Humphreys and Nicol (1995) proposed a similar adaptive algorithm for
UK office temperatures. The gist of his proposed guideline is that a weighted, running
mean of the preceding week’s outdoor temperature is combined with current outdoor
temperature in a ratio of 3:7, thereby reflecting the overriding importance of today’s
weather on clothing decisions and behavior. Humphreys proposed that this outdoor
temperature index be used to specify the target indoor temperature.
Adjustment. Thermal adjustment and behavioral adaptation operate across several
time scales. Cutaneous thermoreceptors provide almost instantaneous neural
information about sudden changes in the thermal environment. For example, as
experienced, when crossing the indoor/outdoor threshold, thus enabling clothing
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 30
adjustments and other behavioral adaptations to be effected well in advance of any
significant alteration in the body’s heat balance. As for other behavioral adaptations,
very little research has been published on adaptive time lags. A notable exception is a
study by Humphreys (1979) on clothing adjustments at the seasonal and synoptic
weather time-scales. He was able to statistically relate clothing insulation levels on any
given day to an exponentially weighted moving average of outdoor temperatures on the
days leading up to, and including, the day in question. It was suggested that the half-life
for daytime clothing regulation was of the order of 20 hours.
Acclimatization. The literature on acclimatization reviewed earlier indicates that the
physiological adaptations to heat exposure begin on the first day of exposure and
progress rapidly to full development by the third or fourth day, providing the heat
exposures are sufficiently severe to elevate core temperatures (Bean and Eichna, 1943;
Fox, 1974). This has been achieved experimentally with daily work-in-heat regimes or
hyperthermic suits. Passive exposures to heat in the course of normal day-to-day
acclimatization cannot be expected to induce acclimatization responses as quickly nor
as thoroughly, although Wyndham (1970) reports that passive exposures to the normal
course of the seasons in South Africa induced definite signs of at least partial
acclimatization. The time-scales of interest for office workers, therefore, may be of the
order of weeks to months.
Habituation and expectations. Unfortunately this literature review was unable to find
reference to any research on the time-scales of psychological adaptive responses,
probably for the simple reason that no researchers have previously attempted to
disentangle psychological from other thermal adaptive processes. However, anecdotal
evidence suggest that building occupants become accustomed to levels of warmth
prevailing within buildings on time scales of weeks to months. These scales translate
into synoptic and seasonal processes operating in the outdoor atmospheric
environment.
To summarize, the adaptive processes are operating on time scales ranging from
seasonal, through synoptic to diurnal. Critics of the adaptive approach at various
symposia or seminars have repeatedly asked the question: “... how long must your
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 31
people suffer in sub-optimal indoor climates before they become adapted?” Ignoring
the emotive language in this question, we feel its answer, if there is one, depends on
which of the adaptive processes is being relied upon. The consensus within what little
has been written on the temporal dimension of adaptation is that meteorological
conditions on the day in question, and to a lesser extent, the preceding week or two,
exert an overriding influence on thermal adaptation in general, and clothing
thermoregulation in particular. This has important implications for future field
experimental protocols. While traditional research designs tend to look at responses at
a given moment, experiments that intend to evaluate adaptive mechanisms need to take
measurements over extended periods of time. Available evidence reviewed in this
paper indicates that, in climate chamber experiments at least, the slower physiological
adaptive process of acclimatization appears not to be relevant to this question of
thermal neutrality and its fluctuations from day-to-day, week-to-week and season-to-
season. As a result, the RP-884 data analysis and model development will focus more
heavily on the adaptive mechanisms of adjustment, and habituation/expectation. This
also suggests the need for field experiments in which data were rigorously obtained,
including accurate measurements of air movement
1.6. Aims
The specific objectives of RP-884 can now be listed:
1. Elaborate and define adaptive processes in the context of indoor climatic
perception.
2. Develop an internally consistent and quality controlled database of thermal comfort
field experimental data from a variety of buildings and climates across the world. To
then make this database as widely available to other thermal comfort researchers
as possible.
3. Examine the semantics of thermal sensation, acceptability and preference scales
within the context of an adaptive model of thermal comfort.
4. Develop statistical models of thermal comfort based explicitly on the various
processes of adaptation, including adjustment, acclimatization and habituation.
ASHRAE RP-884 Final Report
Introduction & Background page MRL Australia 32
5. Explore the influence of contextual and non-thermal factors on thermal perception
indoors. This investigation will include (but not be restricted to) season, building
purpose (residential, office) and climatic setting, on thermal perception. This will
inevitably include comparisons with the thermal comfort predictions of heat-balance
models such as PMV/PPD.
6. Proposing a variable temperature standard that, in time, might eventually
supplement and/or modify ASHRAE Standard 55.
ASHRAE RP-884 Final Report
Methods page MRL Australia 33
CHAPTER 2 - METHODS
2.1. Overview of the RP-884 approach
In view of the vital role played by perceptual and cognitive factors in the adaptive
hypothesis, a consensus emerging from the literature is that observational data to test
the hypothesis must be come from field rather than climate-chamber research. The
reductionist, laboratory approach to comfort research runs the risk of stripping away
those very aspects of thermal perception that are the focus of the adaptive hypothesis
(McIntyre, 1982). The approach in RP-884 has, therefore, been to focus on research
conducted in “real” buildings, occupied by “real” subjects going about their normal day-
to-day activities rather than paid college-age subjects sitting in the highly contrived and
controlled setting of the climate chamber.
In order to identify and disentangle various adaptive processes from the data, it became
apparent in the research design stages of RP-884 that the field data needed to be of a
high standard. The database underpinning RP-884’s adaptive models comprised field
experiments where the standard of measurements, both physical and subjective, was as
close as possible to laboratory-grade, and comprehensive enough to enable heat-
balance indices (static model) to be calculated. Where possible, the RP-884 database
comprised field experiments rather than field studies. Furthermore, the database
needed to be built up from the raw data files generated by the original researchers
instead of their processed or published findings. This approach allowed a variety of
quality controls to be applied and enhanced the internal consistency of the entire
database.
Considerable effort and resources from RP-884 and numerous field researchers around
the world have been dedicated to the assembly of this database of thermal comfort field
experiments. It therefore seems highly likely that the database will have numerous
applications well beyond the scope and lifetime of RP-884. Therefore a decision was
made to provide global and unrestricted access via the World Wide Web (WWW).
ASHRAE RP-884 Final Report
Methods page MRL Australia 34
The ultimate application of any thermal comfort model, adaptive or otherwise, is to
predict the response of a given group of human subjects to a given set of input
parameters (temperatures, humidity, air speeds etc). Typically this means either the
occupants of an extant building, or the hypothetical occupants of a yet-to-be-built
structure. Since RP-884 adaptive models are to be applied at the level of single
buildings, the meta-analysis used to derive the models should be conducted at the same
unit of analysis -- that of the single building. Therefore the 21,000 rows of raw data in the
RP-884 database were subsequently sorted, aggregated and analyzed at the building
level. Figure 2.1 is a schematic depiction of the database process, and how it evolved
into the adaptive model meta-analysis. The remainder of this chapter describes the
detailed steps underpinning this schematic flow chart.
ASHRAE RP-884 Final Report
Methods page MRL Australia 35
Figure 2.1: Schematic depiction of the RP-884 database process and its evolution into the adaptive model meta-analysis.
ASHRAE RP-884 Final Report
Methods page MRL Australia 36
2.2. Establishing the database for RP-884
The RP-884 database is the project’s fundamental research resource. This section
describes where the raw data came from, how they were quality controlled, and what
processes of data assimilation were developed to ensure internal consistency within the
database.
2.2.1. Sourcing the raw data
The literature review in Chapter 1 uncovered numerous thermal field studies and
experiments. Combined with the authors’ and ASHRAE TC 2.1’s knowledge of
researchers currently or recently active in this area, we compiled a mailing list. An initial
fax was broadcast to dozens of researchers around the world requesting information
about field methods and soliciting contributions to the database (see Figure 2.3a and
Figure 2.3b). On the basis of the returns to that questionnaire, a list of the contributors
and their field methods was collated. Figure 2.2 depicts the geographic locations of the
contributors to the RP-884 database. Data came from four continents and a broad
spectrum of climatic zones.
Figure 2.2: Geographic origins of the raw data contributions to RP-884 world database of thermal comfort field research
ASHRAE RP-884 Final Report
Methods page MRL Australia 37
Table 2.1: Sources of raw data for the RP-884 world database of thermal comfort Researcher File
No. Experiment Location Building
Type Research Design
Sample Size
No. of Blgds
Jill Brown (U of Wales - UK) 1 South Wales, UK (summer) HVAC cross-sectional 80 4
Jill Brown (U of Wales - UK) 2 South Wales, UK (winter) HVAC cross-sectional 38 4
John Busch (LBL) 3 Bangkok, Thailand (Hot season) HVAC cross-sectional 776 2
John Busch (LBL) 4 Bangkok, Thailand (Hot season) NV cross-sectional 392 3
Benton + Brager (ACT2) 5 Antioch, California (winter) HVAC longitudinal 111 1
Tri Karyono (Sheffield, UK) 6 Jakarta, Indonesia (summer) HVAC cross-sectional 458 5
Tri Karyono (Sheffield, UK) 7 Jakarta, Indonesia (summer) NV cross-sectional 97 1
Tri Karyono (Sheffield, UK) 8 Jakarta, Indonesia (summer) Mixed cross-sectional 41 1
Donnini ASHRAE RP-821 9 Montreal, Canada (summer) HVAC cross-sectional 443 12
Donnini ASHRAE RP-821 10 Montreal, Canada (winter) HVAC cross-sectional 426 11
de Dear (PhD data) 11 Brisbane, Australia (summer) HVAC cross-sectional 564 5
de Dear (PhD data) 12 Brisbane, Australia (summer) NV cross-sectional 611 5
de Dear (PhD data) 13 Darwin, Australia (dry season) HVAC cross-sectional 493 8
de Dear (PhD data) 14 Darwin, Australia (Wet season) HVAC cross-sectional 555 7
de Dear (PhD data) 15 Melbourne, Australia (summer) HVAC cross-sectional 512 4
de Dear (PhD data) 16 Melbourne, Australia (summer) NV cross-sectional 555 3
Guy Newsham (Canada NRC) 17 Ottawa, Canada (winter) HVAC longitudinal 1859 4
Nicol, Fergus (Oxford-Brooks U) 18 Karachi, Pakistan (summer) NV longitudinal 190 1
Nicol, Fergus (Oxford-Brooks U) 19 Karachi, Pakistan (winter) NV longitudinal 470 1
Nicol, Fergus (Oxford-Brooks U) 20 Multan, Pakistan (summer) NV longitudinal 437 1
Nicol, Fergus (Oxford-Brooks U) 21 Peshawar, Pakistan (summer) NV longitudinal 556 1
Nicol, Fergus (Oxford-Brooks U) 22 Peshawar, Pakistan (winter) NV longitudinal 513 1
Nicol, Fergus (Oxford-Brooks U) 23 Quetta, Pakistan (summer) NV longitudinal 492 1
Nicol, Fergus (Oxford-Brooks U) 24 Quetta, Pakistan (winter) NV longitudinal 425 1
Nicol, Fergus (Oxford-Brooks U) 25 Saidu, Pakistan (summer) NV longitudinal 568 1
Nicol, Fergus (Oxford-Brooks U) 26 Saidu, Pakistan (winter) NV longitudinal 548 1
Nick Baker, Cambridge UK 27 Athens, Greece (summer) NV longitudinal 1626 6
Raja, Ifitkhar (Oxford-Brooks U) 28 Oxford, UK (summer) NV longitudinal 877 3
David Rowe (U Sydney) 29 Sydney, Australia (summer) mixed longitudinal 137 1
David Rowe (U Sydney) 30 Sydney, Australia (winter) mixed longitudinal 170 1
Dav id Rowe (U Sydney) 31 Sydney, Australia (winter) HVAC cross-sectional 83 1
Gail Brager ASHRAE RP462 32 Bay Area, California (summer) HVAC mixed 673 7
Gail Brager ASHRAE RP462 33 Bay Area, California (summer) NV mixed 360 3
Gail Brager ASHRAE RP462 34 Bay Area, California (winter) HVAC mixed 923 7
Gail Brager ASHRAE RP462 35 Bay Area, California (winter) NV mixed 393 3
de Dear & Fountain 702-RP 36 Townsville, Australia (Dry season) HVAC cross-sectional 628 12
de Dear & Fountain 702-RP 37 Townsville, Australia (Wet season) HVAC cross-sectional 606 11
Ruth Williams (BSRIA - UK) 38 Merseyside, UK (summer) NV cross-Sectional 167 3
Ruth Williams (BSRIA - UK) 39 Merseyside, UK (winter) NV cross-Sectional 209 5
Ruth Williams (BSRIA - UK) 40 Merseyside, UK (winter) Mixed cross-Sectional 121 1
de Dear, Foo and Leow 41 Singapore (summer) HVAC cross-sectional 333 1
de Dear, Foo and Leow 42 Singapore (summer) NV cross-sectional 583 1
Bauman et al (Steelcase) 43 Grand Rapids, Michigan (winter) HVAC mixed 85 1
Benton + Brager (ACT2) 44 San Ramon, CA (summer) HVAC longitudinal 96 1
Benton + Brager (ACT2) 45 San Ramon, CA (winter) HVAC longitudinal 285 2
Benton + Brager (ACT2) 46 Auburn, CA (winter) HVAC longitudinal 128 1
TOTAL 20693
TOTAL 160
ASHRAE RP-884 Final Report
Methods page MRL Australia 38
Figure 2.3a: The RP-884 thermal comfort research methods questionnaire sent to active field researchers around the world
ASHRAE RP-884 Final Report
Methods page MRL Australia 39
Figure 2.3b: The RP-884 thermal comfort research methods questionnaire sent to active field researchers around the world
ASHRAE RP-884 Final Report
Methods page MRL Australia 40
2.2.2. Ratings of raw data submitted to RP-884
Field data were classified according to the standard of instrumentation and procedures
used for indoor climatic measurements. Three broad classes of thermal comfort field
investigation were defined as follows:
• Class III: Field studies based on simple measurements of indoor temperature and
possibly humidity. One level of measurement above the floor. Possibly asynchronous
and non-contiguous physical (temperature etc.) and subjective (questionnaire)
measurements. The field studies used to derive the previously published adaptive
models (Humphreys, 1976, 1978, 1981; Auliciems, 1981) were all Class III.
• Class II: Field experiments in which all indoor physical environmental variables (ta,
tr, v, rh, Icl, met) necessary for the calculation of SET* and PMV/PPD indices were
collected at the same time and place as the thermal questionnaires were
administered. Measurements may not have been made at the three heights above
floor level as specified in ASHRAE (1992) and ISO (1994) standards (0.1, 0.6 and
1.2m). Humidity measurements were taken by aspirated psychrometer or solid state
hygrometer sensors. Air speeds were measured by hot wire (or sphere) probes with
thresholds above 0.1 ms-1, directional sensing elements and time constants larger
than that necessary for turbulence intensity, Tu, assessments.
• Class I: Field experiments in which all sensors and procedures were in 100%
compliance with the specifications set out in ASHRAE Standard 55 (1992) and ISO
7730 (1984). In particular, all of the shortcomings identified in Class II investigations
were absent from Class I field experiments. Three heights of measurement with
laboratory-grade instrumentation including omnidirectional anemometry capable of
turbulence intensity assessments. The three ASHRAE-sponsored field experiments
in the San Francisco Bay Area (RP-462), Townsville (RP-702) and Montreal (RP-
921) are examples of Class 1 investigations.
Also listed in Appendix C is a comprehensive summary of each field project adopted in
the ASHRAE RP-884 database. Information listed includes: original researchers’
names, class of data (I, II or III); publications; field location, climate and season;
description of sample buildings; indoor climatic instruments; questionnaire details, and
ASHRAE RP-884 Final Report
Methods page MRL Australia 41
outdoor meteorological/climatological data sources. In addition there is a detailed
section explaining the RP-884 standardization steps and procedures that were applied
to each project’s raw data before they were assimilated into the cumulative database.
2.3. Raw data standardisation
Individual researchers each have their own detailed methods, but thankfully these
idiosyncrasies are largely transparent to the readers of their final research publications.
However, in an exercise involving the assembly of a database from raw data, the
emphasis must be on standardization. In the present case this has not been easy since
the decision to assemble a database occurred after the original data were collected
(except in the case of the ASHRAE field RPs). This section describes some of the
more important steps in this process of data assimilation.
2.3.1. Creation of a standard data template
A standard template of variables was developed, based on previous ASHRAE-funded
research projects, particularly RP-702 (hot-humid), RP-462 (Mediterranean) and RP-
821 (cold climate). This template was applied to each and every row of data in the RP-
8884 database (n~21,000). The standard template consisting of units of measurement,
codenames and coding conventions is presented in Appendix E. The template is
broken down into the following groups of variables:
• Basic Identifiers such as building code, subject information and date.
• Thermal Questionnaire comprising sensation, acceptability and preference scales,
as well as activity, metabolic rates, clothing and chair insulation.
• Indoor Climate Physical Observations of air temperature, globe temperature, air
velocity and turbulence at three heights, plus dewpoint, rh and plane radiant
asymmetry temperature.
• Calculated Indices, including averaged single height measurements of air
temperature, mean radiant temperature, air velocity; operative temperature,
turbulence intensity, vapour pressure and relative humidity; new effective temperature,
new standard effective temperature, TSENS, DISC, predicted mean
vote, predicted percentage dissatisfied and draft risk at three heights and maximum.
ASHRAE RP-884 Final Report
Methods page MRL Australia 42
• Personal Environmental Control covering questions of perceived control (including a
composite index that could be applied to buildings where the questionnaire did not
cover perceived control), and specific adaptive opportunities. Options include:
windows, internal doors, external doors, thermostat, curtains/blinds, local heaters and
fans.
• Outdoor Meteorological Observations include raw data and derived indices. Daily
temperatures and relative humidities at 600 hours and 1500 hours were collected,
and daily effective temperatures (ET*) for these times calculated with WinComf©
(Fountain and Huizenga, 1996). Daily averages for air temperature, relative humidity
and effective temperature were also calculated.
2.3.2. Consistent mean radiant temperatures within the database.
Mean radiant temperature was recalculated from each row of data using the ASHRAE HoF
formula (1993), based on raw globe and air temperatures plus air speed.
t (t 273)1.10 10 V
D(t t ) 273
r g4
8 0.6
0.4 g a
14
= + −
−+
•
•ε
where ε is emissivity (0.95 for a black globe), D is globe diameter (0.04 m for “ping-pong”), V is air speed in m s-1, ta is air temperature in oC, tg is globe thermometer’s temperature in oC. N.B. the globe thermometer has a lagged response and requires about 10 to 15 minutes to equilibrate. Larger diameter globes have longer lags.
2.3.3. Consistent comfort index calculations within the database
With models as complex as PMV and SET*, it is to be expected that several different
algorithms and implementations exist in engineering and research circles around the
world. ASHRAE TC 2.1 has recently acknowledged this potential source of “noise” in
comfort research and engineering applications, and has sought to standardize the
models into a single software package (ASHRAE RP-781) now known as WinComf©
ASHRAE RP-884 Final Report
Methods page MRL Australia 43
(developed by Fountain and Huizenga, 1996). From this software, new effective
temperature (ET), new standard effective temperature (SET), the two-node temperature
sensation index (TSENS), the two-node discomfort index (DISC), Fanger’s Predicted
Mean Vote (PMV) and Fanger’s Predicted Percentage Dissatisfied (PPD) were
obtained for each row of data in each field experiment within the database, provided the
necessary input data were available. Requisite input data include: air temperature
(average of three heights, TAAV), mean radiant temperature (average of three heights,
TRAV), air speed (average of three heights, VELAV), relative humidity (RH), metabolic
rate (MET) and insulation afforded by clothing and chair (INSUL). Where only one
measurement height was available in the raw data files, it was treated as the average. If
TRAV values were missing TAAV was often substituted in its place.
2.3.4. Predicted draft risk index (PD)
Predicted Draft Risk was not taken from the WinComf© software. The following formula
was used:
PD = (34 - ta) * (v - 0.05)0.62 * (0.37 * v * Tu + 3.14)
ta = indoor air temperature (oC)
v = indoor air velocity (m s-1)
Tu = turbulence intensity (%)
ta, v and Tu were provided at three heights (0.1m, 0.6m and 1.1m above the floor).
Where possible PD was calculated at the three heights and designated as PD_L,
PD_M and PD_H. The maximum of these was then taken as the value of the index
(PD_MAX) for subsequent analyses. Where ta, v and Tu were only recorded at one
height the resulting PD defaulted to PD_MAX. Where no turbulence was provided in the
raw data files, the default of 40% was substituted in calculations and if the velocity was <
0.05 m s-1 the default value for PD was zero.
ASHRAE RP-884 Final Report
Methods page MRL Australia 44
2.3.5. Clothing insulation in the ASHRAE RP-884 database
Clothing insulation has remained one of the more troublesome parameters for field
researchers. It is often singled out as an explanation whenever observed comfort responses
of building occupants depart from the predictions of thermal comfort standards and indices.
The insulation value of a particular ensemble of clothing can only be measured with any
precision using a thermal manikin in a controlled climate chamber over several hours, and
even then the answer varies considerably between manikins. Little wonder, therefore, that
the estimation of clothing insulation has presented HVAC practitioners with confusion when
applying comfort theory in the field, encouraging them to think of thermal comfort
simplistically in terms of single set-point temperatures instead of multidimensional comfort
zones.
But it is not only practitioners who have trouble with clothing insulation -- even researchers
face difficulties due to a complicated variety of garment insulation databases and equations
available to derive whole ensemble insulation values. Adding further confusion, even the
standards themselves (ISO 7730 and ASHRAE 55) have recommended different techniques
and equations between their various revisions. This leads to the surprising situation where a
given set of clothing may get quite different clo estimates attached to it, depending on which
standard and which edition is used.
From the RP-884 perspective, clothing represents one of the key thermal adaptive
responses. Clothing is a behavioral adjustment that directly affects the heat-balance.
Therefore this important parameter demanded careful treatment within the RP-884
database. The main approach here has been to establish statistical “conversion
factors” between the various clo estimation techniques based on a large sample of
clothing data from the ASHRAE RP-462 project in San Francisco (Schiller et al.,
1988a,b), and to use these to convert all clo data within the RP-884 database into
equivalent ASHRAE Standard 55-92 (ASHRAE, 1992) intrinsic ensemble clo
estimates.
ASHRAE RP-884 Final Report
Methods page MRL Australia 45
2.3.5.1. Discrepancies between field estimation methods for clo.
Brager et al. (1994) indicated that conversions from ASHRAE Standard 55-81 to 55-92 clo
estimates lifted their sample average ensemble insulation by 0.1 clo, alerting the RP-884
team to the possible difficulties in comparing field data from several different researchers
around the world. Because its raw data file, as supplied to the RP-884 team, contained
individual clothing garment values (CL1 through CL15) instead of the usual aggregate
ensemble insulation estimates, the ASHRAE RP-462 project (Schiller et al 1988a,b)
afforded a unique opportunity to quantitatively compare clothing ensemble insulation
estimates based on several different techniques with each being benchmarked against the
ASHRAE Standard 55-1992 method:
• Sprague and Munson (McIntyre 1980),
• ASHRAE Standard 55-1981,
• ISO7730 1984,
• ISO7730 1994,
For each subject in the RP-462 field experiment spreadsheets, we estimated the
ensemble clothing insulation using each of the techniques listed above, and also the
ASHRAE 55-1992 technique. Following are some regression models fitted to the
relationships between the ASHRAE 55-92 estimates, and the other four techniques in
turn. The two genders were analyzed separately and all regression equations were
forced through the zero origin so that the fitted models could be applied throughout the
RP-884 database as simple conversion coefficients.
ASHRAE RP-884 Final Report
Methods page MRL Australia 46
Male Clothing Estimate from RP-462 data
ASH55-92 = 1.0938 * ASH55-81R2 = 0.8072
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
ASHRAE 55-81 clo
AS
HR
AE
55-
92 c
lo
Female Clothing Estimate from RP-462 data
ASH55-92 = 1.2362 * ASH55-81
R2 = 0.6073
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6
ASHRAE 55-81 clo
AS
HR
AE
55-
92 c
lo
Figure 2.4: Relationships between ASHRAE 55-81 and ASHRAE 55-92 clo estimates in the RP-462 field experiment
Figure 2.4 depicts the ASHRAE 55-81 ⇒ ASHRAE 55-92 models for the RP-462 data
files. The male samples’ model had 832 data points and was highly significant
(F=3,478; p=0), explaining 81% of variance (r = 0.90). The actual model had a
regression coefficient of 1.094 (95% confidence interval 1.086 - 1.102), indicating that
clothing insulation estimates using the new method described in ASHRAE Standard 55-
92 were, on average, 9.4% higher than those obtained for the same male subjects using
the ASHRAE 55-1981 methods.
The RP-462 female samples’ model had 1,508 data points in Figure 2.4 and was also
highly significant (F=2,330, p=0) with 61% explained variance (r = 0.78). The regression
coefficient was 1.236 (95% confidence interval 1.224 - 1.249), indicating that clothing
insulation estimates using the ASHRAE Standard 55-92 estimation method were, on
average, 23.6% higher than those obtained using the ASHRAE 55-1981 methods.
ASHRAE RP-884 Final Report
Methods page MRL Australia 47
Male Clothing Estimate from RP - 462 data
ASH55-92 = 1.1979 * McIntyre2 + 0.219 * McIntyre
R2 = 0.9166
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0 1.5 2.0 2.5
McIntyre (Sprauge and Munson) CLO
AS
HR
AE
55
- 92
CLO
Female Clothing Estimates from RP - 462 data
ASH55-92 = 1.0921 * McIntyre
R2 = 0.7851
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0 1.5 2.0 2.5
McIntyre (Sprauge and Munson) 1980 CLO
AS
HR
AE
55
- 92
CLO
Figure 2.5: Relationships between Sprague & Munson (McIntyre 1980) and ASHRAE 55-92 clo estimates in the RP-462 field experiment
Figure 2.5 depicts the regression models fitted to the relationship between the Sprague
and Munson clo method (reported in McIntyre, 1980) and the ASHRAE 55-92 method.
The curvilinear relationship for male subjects was best approximated by a 2nd order
polynomial regression model which managed to account for 92% of the variance in
Standard 55-92 estimates (r=0.96). The female subjects in RP-462 had their ASHRAE
55-92 clothing ensemble insulation estimates were systematically larger than the
Sprague and Munson estimates by a factor of 9.2% and the linear relationship between
the two estimation methods had a correlation coefficient of r=0.89.
Male Clothing Estimates from RP-462 data
ASH55-92 = 0.3839 * ISO-842 + 0.6579 * ISO-84R2 = 0.9518
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0 1.5 2.0 2.5
ISO 7730 1984 CLO
AS
HR
AE
55
- 92
CLO
Female Clothing estimates from RP-462 data
ASH55-92 = 1.057 * ISO-84R2 = 0.8476
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0 1.5 2.0 2.5
ISO 7730 (1984) clo
AS
RA
E 5
5-92
clo
Figure 2.6: Relationships between ISO 7730 (ISO, 1984) and ASHRAE 55-92 clo estimates in the RP-462 field experiment
Figure 2.6 depicts the relationships between RP-462 clothing ensembles insulation
estimates using the ISO 7730 (1984) and ASHRAE 55-92 methods. The male subjects’
ASHRAE RP-884 Final Report
Methods page MRL Australia 48
clothing was described by a second order polynomial regression which explained
95.2% of the variance (r=0.98). The female subjects’ model was a simple linear
regression with the ASHRAE 55-92 estimates being, on average, 5.7% higher than the
ISO 7730 (1984) estimates, and the relationship accounting for 84.8% of variance
(r=0.92).
Male Clothing Estimate from RP - 462 data
ASH55-92 = 0.3954 * ISO-942 + 0.6954 * ISO-94
R2 = 0.9448
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0 1.5 2.0 2.5
ISO 7730 -1994 CLO
AS
HR
AE
55-
92 C
LO
Female Clothing Estimates from RP-462 data
ASH55-92 = 1.0049 * ISO-94
R2 = 0.8814
0.0
0.5
1.0
1.5
2.0
2.5
0.0 0.5 1.0 1.5 2.0 2.5
ISO 7730 - 1994 CLO
AS
HR
AE
55-
92 C
LO
Figure 2.7: Relationships between ISO 7730 (ISO, 1994) and ASHRAE 55-92 clo estimates in the RP-462 field experiment
Figure 2.7 above depicts the regression relationships between RP-462 clothing
ensemble insulations values estimated by the ISO-7730 2nd edition (1994) and
ASHRAE 55-92 methods. The male subjects’ clo estimates were approximated again
with a second order polynomial which accounted for 94.5% of the variance (r=0.97).
The females’ regression model was a simple linear one (r=0.94) with the ASHRAE 55-
92 clo estimates being about half a percent higher than the ISO 7730 (1994)
counterparts.
Unlike the RP-462 raw data files, the remainder of the raw data contributions to
ASHRAE RP-884’s database contained only total ensemble insulation estimates,
therefore ruling out any systematic garment-by-garment conversions and trends of the
type performed for RP-462 above. However, a preliminary questionnaire sent to each of
the RP-884 database contributors enquired about their method of clo estimation (see
Figure 2.3a and Figure 2.3b). Where the method used was pre-ASHRAE Standard 55-
1992, the original researchers’ clo estimates in their raw data file were simply scaled up
or down to equivalent Standard 55-92 levels using the conversion factors (regression
models) described above in Figures 2.4 through 2.7.
ASHRAE RP-884 Final Report
Methods page MRL Australia 49
2.3.5.2. The chair insulation effect
The preliminary questionnaire on field methods sent to all database contributors (Figure
2.3a and Figure 2.3b) also enquired into whether or not the incremental insulation effect
of furniture was included in their clo estimates. If omitted from the original estimates, an
additional 0.15 clo was added, after the regression correction to clothing insulation had
been performed (McCullough and Olesen, 1994; de Dear, 1994). While it is recognised
that all chairs, stools, sofas, and any other horizontal surface which might have acted as
a chair at the time of questionnaire for the 21,000 subjects in the RP-884 database may
not have provided exactly 0.15 clo insulation at the time of interview, we feel inclusion of
this “best estimate” is preferable to omitting the effects of chairs altogether.
2.4. Developing an index for perceived thermal control
Adaptive opportunity and perceived control figured prominently throughout the literature
review in Chapter 1, but unfortunately, only a handful of original field experiments
supplied to the RP-884 database actually recorded these data in their survey buildings.
Therefore the development of a method for estimating this parameter across all
buildings within the RP-884 database was given a high priority. This section describes
the assumptions and steps we made to achieve this goal.
• Step 1: Find a data base possessing both a global perceived control item (PCC) in
the original questionnaire as well as individual items on specific adaptive
opportunities (PCEC1 through PCEC7). The ASHRAE-sponsored RP-702 (hot-
humid Townsville) and RP-821 (Cold climate Montreal) fulfilled these requirements.
• Step 2: Classification of the adaptive opportunities (PCEC variables) according to
their relevance to season (See Table 2.2). For example, access to windows was not
regarded as a relevant thermal control during winter months, whereas access to
thermostats was deemed relevant all year round.
ASHRAE RP-884 Final Report
Methods page MRL Australia 50
Table 2.2: Thermal adaptive opportunities classified according to their relevance to perceived control in summer and winter seasons
PCEC variables Can You Control Season
PCEC1 windows summer
PCEC2 external doors summer
PCEC3 internal doors summer/winter
PCEC4 thermostats summer/winter
PCEC5 curtains/blinds summer/winter
PCEC6 local heaters winter
PCEC7 local fans summer
• Step 3: For the summer index, we found all cases in the database that had control
over operable windows ( PCEC1= 1) but none of the other adaptive opportunities
(PCEC2 through PCEC7). We then found the average of the overall perceived
control variable (PCC) for this subset of the database with control over windows.
Similar perceived control averages for each of the other adaptive opportunities
(PCEC variables) were obtained
• Step 4: For each of the 21, 000 subjects in the RP-884 database, we summed the
relevant perceived control scores (Step 3) for all adaptive opportunities they had at
their disposal. The aggregate score resulting from this step was entered in the
database as PCC_AG.
We can see from Table 2.3 that, in both summer and winter, the most efficacious
adaptive opportunity is “thermostats,” with “internal doors,” “curtains/blinds,” “external
doors,” “local heaters” and “local fans” all rating approximately equal for their designated
season. In summer, windows also contribute significantly to building occupants’
perceived control.
ASHRAE RP-884 Final Report
Methods page MRL Australia 51
Table 2.3: Thermal adaptive opportunities scored according to their influence on perceived thermal control in summer and winter
PCEC variables Can You Control PCC score in
Summer season
PCC score in Winter
Season
PCEC1 windows 1.6
PCEC2 external doors 1.3
PCEC3 internal doors 1.3 1.3
PCEC4 thermostats 1.8 2.0
PCEC5 curtains/blinds 1.3 1.4
PCEC6 local heaters 1.3
PCEC7 local fans 1.5
2.5. Thermal acceptability issues within the RP-884 database
2.5.1 Developing a proxy variable for thermal acceptability based on thermal sensation
votes.
The thermal comfort standards such as ASHRAE’s Standard 55 and ISO 7730 are
couched in terms of maintaining certain levels of thermal acceptability within a
building. Unfortunately specific questionnaire items on thermal acceptability such as this
(TSA):
“Is this environment thermally acceptable to you at this point in time?”
were available in only a small subset of buildings in the RP-884 database, but a proxy
could be inferred from thermal sensation votes (ASH), which were recorded in all
studies. It has generally been assumed that a thermal sensation vote within the central
three categories of the ASHRAE scale is acceptable. Translating to the real-number
version (as opposed to the integer only version) of the scale, this criterion for
acceptability was defined as a thermal sensation vote falling in the interval -
1.5<ASH<+1.5. Applying this criterion to the RP-884 database, we simply tallied the
number of subjects within each building registering an acceptable thermal sensation
vote, and expressed it as a percentage of the total sample size for that particular
building (designated as fprxysat in the RP-884 codebook, Appendix E). The resulting
ASHRAE RP-884 Final Report
Methods page MRL Australia 52
percentages can be treated in the meta-analysis as a thermal acceptability rating for the
building in question.
2.5.2. Rating buildings in terms of their compliance with ASHRAE Standard 55
acceptable indoor climate guidelines
If we are prepared to ignore the upper and lower humidity boundaries of the summer
and winter comfort zones depicted in ASHRAE Standard 55-92 (which may be justified
in view of the ongoing debate as to what they should actually be -- see Berglund, 1995),
it was a relatively simple task to assess each RP-884 database building’s percentage
of indoor climate measurements falling within either the summer or winter ASHRAE
comfort zone (ASH55_92). This also depends on which season the building was
surveyed in. It was done for buildings assessed during the cooling season (summer),
with:
23°C <= indoor ET* <= 26°C
and for the heating season (winter), with:
20°C <= indoor ET* <=23.5°C
The resulting percentages for each building can be regarded in the RP-884 meta-
analysis as an index of the compliance with the ASHRAE Standard 55 thermal
acceptability prescriptions.
2.6. Outdoor meteorological/climatological data for the data base
Obviously outdoor weather and climate represent key components of any conceivable
adaptive model of thermal comfort since outdoor climate partly drives acclimatization,
behavioral and psychological adaptive responses.
2.6.1. Appending outdoor weather observations to each row of data
For those studies supplied to the RP-884 database without weather data, the first
priority was to obtain meteorological data (weather data recorded on exactly the same
dates as the indoor observations). If that was not possible, then climatological data
were used (i.e. data from published sources covering long-term statistical averages for
the months in question). The outdoor atmospheric parameters collected for the RP-884
database consisted of daily outdoor air temperature and coincident relative humidity at
ASHRAE RP-884 Final Report
Methods page MRL Australia 53
6:00 am and 3:00 pm. These times were selected because they represent typical times
of occurrence of daily minimum and maximum temperature. The times also typically
correspond with daily maximum and minimum relative humidity.
Various national weather and climate data resources searched and used included:
• The US National Climatic Data Center (NCDC) which currently maintains an on-line
US climatic data archive on the World Wide Web (INTERNET) from which it is
possible to download data via the Hyper-Text Transfer Protocol (HTTP).
• Commercially available CD ROMS such as the International Station Meteorological
and Climate Summary (ISMCS, 1992) proved most useful in filling some of the gaps
in the RP-884 meteorological/climatological data base.
• Published Climatological data resources such as in academic journal Weather were
used to obtain maximum and minimum temperatures (with relative humidity supplied
by ISMCS) for many of the UK field experiments.
• In two cases, meteorological data were supplied gratis by weather stations on
university campuses. These were the Radcliffe Observatory at Oxford University, UK,
and the Physical Geography Met Site at Macquarie University in Sydney Australia.
• For those investigations in which the actual outdoor meteorological data were either
unavailable from previously listed sources, or at the wrong temporal resolution, it was
necessary approach the relevant State or National Climatologists (weather bureaux)
for raw data. This was done for the Australian field experiments in Brisbane, Darwin
and Melbourne, for all the Californian field experiments, and the Steelcase project in
Michigan.
2.6.2. Climate classification applied to RP-884 raw data
A relatively simple and descriptive climate classification developed for the Macquarie
University undergraduate teaching program in climatology was applied to the RP-884
database. A map of the classification can be seen in Appendix D.
ASHRAE RP-884 Final Report
Methods page MRL Australia 54
2.7. Subdivision of the standardized field experiments
Once the field experiments supplied by original researchers had been quality controlled
and standardized into the RP-884 database template, they were broken down
according to season (summer/winter) and building type (centrally controlled HVAC
buildings, naturally ventilated buildings NV, and mixed-mode buildings). Here the
distinction between centrally-controlled HVAC buildings and naturally ventilated
buildings is that in central HVAC buildings individual occupants have little or no control
over their imediate thermal environment, while occupants in naturally ventilated buildings
at least have control over operable windows. See Table 2.1 and the “sample buildings”
sections for of each project summary in Appendix C.
2.8. The meta-analysis
By aggregating the statistical unit of analysis up from the individual subject to whole
buildings, the RP-884 was able to reduce the 21,000 cases in the database to 160
buildings. This section describes how the aggregation was performed and how the
resulting meta-file was used as the basis for developing adaptive models.
2.8.1. The unit of analysis for the RP-884 meta-analysis
Earlier attempts at defining adaptive models (Humphreys and Auliciems) typically
aggregated data up to the unit of an entire field study, which often incorporated many
different buildings. Therefore the early adaptive models may have glossed over
considerable variety in contextual factors affecting subjective responses. While each
record within the RP-884 database was structured as one individual subject’s thermal
questionnaire, indoor climatic physical measurements, thermal index values and outdoor
meteorological observations, the most appropriate unit of analysis for the statistical
modelling part of the project is the single building. This level of aggregation masks
some of the inherent noise involved in a single subject’s thermal comfort assessment,
while still providing sufficient data points for statistical modelling purposes.
Furthermore, several important parameters such as neutrality and preferred temperature
can only be sensibly derived from a group’s response. Data analysis at the level of
ASHRAE RP-884 Final Report
Methods page MRL Australia 55
buildings rather than individuals also ensured a modicum of consistency across several
contextual factors relevant to thermal adaptive processes, including :
• type of HVAC system,
• degree of personal environmental control,
• job satisfaction and other managerial factors that might impinge upon thermal
comfort,
• temporal variability of internal temperatures in the days/weeks preceding the thermal
comfort experiment,
• mean levels of outdoor meteorological factors and their variability in the days/weeks
preceding the comfort experiment.
In total there are 160 individual buildings in the RP-884 database.
2.8.2. Meta-file’s structure and coding conventions
The meta-file included country, city, and season in which the field experiment was
conducted. Data quality and intensity of measurement were also recorded, as was
building type (HVAC, NV, mixed-mode). Following these descriptors are means and
standard deviations of questionnaire responses (e.g. ASHRAE sensation votes and
thermal environmental measurements plus derived indices). In addition there are the
derived products such as the building’s observed thermal neutrality, preferred
temperature and thermal acceptability rating. The full listing of variables in the meta-file
and their coding conventions can be found in Appendix F.
2.8.3. General assumptions within the statistical meta-analysis
• For the purpose of statistical analysis in RP-884, field experiments with longitudinal
research design (few subjects, sampled many times) were assumed to have
independence between subjects. That is, longitudinal studies were treated the same
way as cross-sectional research designs during the meta-analysis. We also
accepted that all other statistical assumptions of linearity, normality and equality-of-
variance applied across the data base.
ASHRAE RP-884 Final Report
Methods page MRL Australia 56
• For all statistical modelling conducted on the meta-file, each building “data point” was
weighted according to the number of human subjects it represented (i.e. sample size
within the building). The purpose of using a weighting factor was to minimise the
impact of outlying data points that were based on relatively small number of
observations.
• Statistical products such as building neutrality or preferred temperature were
appended as new variables in the meta file. However, if the statistical model or test
in question failed to reach statistical significance at the p=0.05 level of or better, the
building registered a missing value code for that particular variable in the meta file.
• Test statistics based on small sample sizes were interpreted with care or eliminated
(i.e. coded as “missing values”) due to their wide confidence interval estimates.
2.8.4. Statistical treatments on the various subjective thermal ratings
There are some common features in the methods used in thermal comfort field work,
particularly in relation to their assessmetns of subjective warmth within buildings. The
most common approach has been the rating scale method in which comfort is
operationalized as a vote of "neutral" or "comfortable" on scales such as those
depicted in Table 2.4. Shading has been used in the table to indicate the commonly
assumed mapping between rating scales and other thermal assessments. That is,
“neutral” is generally assumed within the comfort research community to be synonymous
with “comfortable”, “acceptable,” and “preferred.”
Despite the apparent semantic differences between the ASHRAE scale of thermal
sensation and the Bedford comfort scale, these two scales have been found to behave
more-or-less the same in most practical situations (McIntyre, 1978a; de Dear, 1985).
This encouraged direct comparisons in this project between studies using either scale.
But recent analyses of questionnaire studies in which acceptability, preference and
thermal sensation were recorded simultaneously reveal that the optimum temperature
based on thermal sensation votes does not correspond exactly with that derived from
thermal preference or acceptability (Brager, 1994). Therefore thermal acceptability and
preference were analyzed separately wherever possible.
ASHRAE RP-884 Final Report
Methods page MRL Australia 57
Table 2.4: Common rating scales used in comfort research in the field
ASHRAE scale Bedford scale Acceptability Preference (McIntyre)3 hot much too warm2 warm too warm unacceptable want cooler1 slightly warm comfortably warm0 neutral comfortable acceptable no change
-1 slightly cool comfortably cool-2 cool too cool unacceptable want warmer-3 cold much too cool
The ambient temperature found by statistical analysis to most frequently coincide with
the central, usually “neutral” or “comfortable,” rating in a thermal comfort study is referred
to as that sample's "neutrality". Neutrality was calculated in the meta-analysis by the
following steps:
• Binning a particular building’s observations into half-degree (K) increments, and
working with the bins’ mean response, say thermal sensation vote, instead of
individual subjects’ thermal votes.
• Fitting a linear regression model between thermal sensations and whatever the x-axis
thermal index may be (TOP, ET, SET, PMV). The regression models weighted each
point according to the number of observations within each x-axis bin. The regression
models had the general form:
mean thermal sensation = a + b * (bin index value)
The following statistical details of each building’s four regression models (TOP, ET,
PMV, SET) were extracted for the meta-analysis:
• gradient (b) of each regression model, a measure of thermal sensitivity,
• the neutrality of the model, i.e. solution of the linear equation for a mean thermal
sensation value of zero, or “neutral,”
• the range of index values corresponding with 80% “acceptable” thermal sensations,
i.e. the distance between solutions of the linear equation corresponding with mean
ASHRAE RP-884 Final Report
Methods page MRL Australia 58
thermal sensations of -0.85 (close to the mean vote of “slightly cool”) and +0.85
(close to “slightly warm”),
• The range of index values corresponding with 90% “acceptable” thermal sensations
between solutions of the linear equation corresponding with mean thermal sensations
of -0.5 and +0.5.
These boundaries were selected on the assumption that the normal distribution of
thermal sensations recorded within each building resembled that of Fanger’s PPD
function (1970). So a mean vote of ±0.85 was assumed to correspond with 80%
general acceptability (20% dissatisfaction, excluding local discomfort), ie. 80% of votes
falling inside the central three categories. A mean vote of ±0.5 was assumed to
correspond with 90% general acceptability (10% dissatisfaction).
Apart from statistically deriving observed neutralities for each building with the
procedures above, the meta-file also contains predicted neutralities for each building on
the basis of heat-balance models. The model used for this purpose was Fanger’s
(1970) PMV index, and it was applied to the problem in the following way:
• Each building’s mean values for each of the five PMV variables (TOP, RH, VEL,
INSUL, MET) were input to the WinComf© software (Fountain and Huizenga, 1996).
• The PMV model was solved iteratively by adjusting TOP (ta with tr linked) until the
PMV output field equalled zero. The final operative temperature corresponding with
PMV=0 is, by definition, predicted neutrality (PREDNEUT) for that particular building.
• One additional variable named DELTNEUT was derived from the PMV model -- the
difference between observed and predicted neutralities (NEUT_TOP and
PREDNEUT respectively). PREDNEUT was subtracted from NEUT_TOP, so that if
a particular buildings occupants were neutral in temperatures warmer than expected
by the PMV model, their DELT_NEUT was positive in sign.
2.8.5. Preferred temperatures
Preferred temperature was assessed directly (MCI) in a subset of the buildings in the
RP-884 database. The typical questionnaire item was of the type:
ASHRAE RP-884 Final Report
Methods page MRL Australia 59
“At this point in time, would you prefer to feel warmer, cooler, or no change?”
These categorical data require different statistical treatments to that applied to linear
ASH scale of thermal sensation. In particular, probit analysis (Finney, 1971; Ballantyne
et al, 1977) is applicable rather than linear regression. However, probit requires binary
responses, whereas the questionnaire item described here has three possible answers.
The solution was to split the “no change” responses 50:50 into the remaining two
categories. Statistical software was applied to the task of tallying the number of
observations with MCI=1 (“want cooler”), and MCI=3 (“want warmer”) for each half-
degree temperature bin. Separate probit models were fitted to each of the “want
warmer” and “want cooler” percentages with the SAS probit procedure. Our operational
definition for the preferred temperature (or other index) within a particular building is that
value of the independent variable (e.g. operative temperature) corresponding to the
intersection of the “want cooler” and “want warmer” probit models. The fitted probit
models and preferred temperatures are depicted in separate graphs in Appendix B for
each building in which the MCI questionnaire item was available. Only those models in
which the probit models achieved statistical significance at the p=0.05 level or better
had their preferred temperatures registered in the RP-884 meta-file.
The RP-884 work statement specified separate analyses of thermal comfort (assumed
to equal sensation) and preference. Part of the logic underpinning this distinction is
known as the “semantic artefact hypothesis” which suggests that the preferred
temperature in cold climates may in fact be described as “slightly warm,” whereas
residents of hot climates may use words like “slightly cool” to describe their preferred
thermal state. While the actual temperatures preferred in both climatic extremes may in
fact be identical (assuming similar clothing, air speed, metabolism etc), the semantics
may differ to such an extent that the neutrality derived from thermal sensation scales in
the manner described above could shift up in warm climates and down in cold climates.
The RP-884 meta-file offers an opportunity to examine the semantic artefact in some
detail, since there were 55 buildings in which both thermal sensations (ASH) and
thermal preferences (MCI) were assessed. To this end, a new variable was defined in
meta-analysis:
ASHRAE RP-884 Final Report
Methods page MRL Australia 60
semantic discrepancy = neutrality minus preferred temperature (°C)
discrep = neut_top - preftemp (°C)
2.9. The RP-884 database in the public domain and disseminated via the World
Wide Web
The ASHRAE RP-884 project has its own homepage on the World Wide Web at the
following URL:
http://atmos.es.mq.edu.au/~rdedear/ashrae_rp884_home.html
The homepage, depicted in Figure 2.9, serves the purpose of introducing the RP-884
project, in particular the main team members on the project as well as the overall
concept of “adaptive models” in the context of thermal comfort research (Fig. 2.10).
The website also describes the background to the RP-884 database, linking to a flow
chart outlining the processes of data acquisition, quality control, standardization and
assimilation (Figure 2.1 is hyper-linked in the homepage). The structure of the database
and a copy of the codebook (Appendix E) are covered in another hyperlink to the
homepage. Most importantly, the comfort research community is given access to the
entire RP-884 database by means of an FTP server presented as a clickable “data
downloader” on the RP-884 website (see Figure 2.11). The table enables a total of 46
separate data files, each in a variety of formats, to be downloaded from the RP-884 host
machine in Sydney to any PC, Mac or UNIX machine elsewhere in the world, as long as
it is connected to the internet. Several data formats are available in an effort to facilitate
cross-platform transfers, but the most heavily used format is MS Excel® V.5
spreadsheets for use within the MS Windows ® 3.X or Windows 95 operating
environments. These data files have been “zipped” into compact, self-extracting
archives with *.exe filenames. The user will need to execute (run) the *.exe file after
it has been transferred and it will automatically inflate back to the native Excel® 5 format,
ready for use on the user’s machine with an *.XLS filename. The forenames of the 46
files within the “data downloader” correspond to the file numbers listed in Table 2.1
earlier in this chapter.
ASHRAE RP-884 Final Report
Methods page MRL Australia 61
Figure 2.9: The homepage for RP-884 on the World Wide Web http://atmos.es.mq.edu.au/~rdedear/ashrae_rp884_home.html
ASHRAE RP-884 Final Report
Methods page MRL Australia 62
Figure 2.10: One of the pages linked to the ASHRAE RP-884 homepage
ASHRAE RP-884 Final Report
Methods page MRL Australia 63
Figure 2.11: The entire RP-884 database (46 data files) is accessible to anyone who is interested via
this “data downloader. The device is a “clickable form” interface and can be found on the RP-884 project’s website.
ASHRAE RP-884 Final Report
Methods page MRL Australia 64
2.10. Summary of the methods used in RP-884
This chapter has described the RP-884 approach to developing adaptive models of
thermal comfort and preference. Underpinning the method has been the creation of a
large database of thermal comfort field research observations. The raw data for this
database were assembled from a wide variety of climatic, geographic and architectural
contexts, and at last count, the database had in excess of 21,000 rows of raw data. The
database is made available to the thermal comfort R&D community via a homepage on
the World Wide Web.
The raw data supplied to the RP-884 database included basic characteristics of the
building in which each subject was interviewed, demographic descriptors, the subject’s
thermal sensation, preference and acceptability votes at the time of the indoor climate’s
physical measurements (ta, tr, rh, v, Tu, clo, met). In view of the significance of clothing
in terms of behavioural thermal adjustments and also various thermal index calculations,
particular care was taken to ensure clothing and furniture insulation values were derived
from, or converted to, a consistent estimation method throughout the entire RP-884
database -- the ASHRAE Standard 55-92 method was selected as the benchmark for
this purpose.
Once raw data had been standardized, cleaned and assimilated into the database,
thermal indices such as ET*, SET, PMV, PPD, PD were calculated using a standard
software tool (ASHRAE RP-781). In addition, outdoor meteorological and
climatological observations were appended to each set of data in the database to
enable an examination of the role played by outdoor atmospheric environmental factors
in thermal adaptation.
The meta-analysis for RP-884 was conducted on this large database by aggregating
observations up to the level of individual buildings, of which there were 160 in total.
Statistical results were derived at this level of aggregation, including the building’s
thermal neutrality, preferred temperature, thermal acceptability rating, mean indoor
ASHRAE RP-884 Final Report
Methods page MRL Australia 65
thermal index values, as well as mean outdoor climatic indices at the time of the
building’s survey.
The next chapter (3) describes the basic results of this meta-analysis. These then
provide the foundation upon which adaptive models of thermal comfort will be built in
Chapter 4.
ASHRAE RP-884 Final Report
Methods page MRL Australia 66
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 67
CHAPTER 3 - BASIC RESULTS
The generic term “thermal comfort” covers many aspects of subjective thermal
experience, each of which has been operationalized with specific questionnaire items
by researchers over the years. This chapter focuses on three specific dimensions of
thermal perception: a) thermal sensation, b) thermal acceptability, and c) thermal
preference. The chapter relates these subjective data to; a) indoor climate, b) outdoor
climate, and c) various built environmental-contextual factors.
3.1. Interactions with Indoor Climate
This section examines thermal perceptual data in relation to indoor climatic parameters.
The first subsection deals with thermal sensation (ASH) and derived parameters such as
thermal neutrality and their statistical associations with indoor climatic factors. The second
subsection examines thermal acceptability, either directly measured or inferred from (ASH),
while the third subsection examines thermal preferences.
3.1.1. Thermal sensation
Thermal sensation (ASH) within each building was analyzed separately with respect to four
indices of indoor warmth:
• operative temperature (TOP)
• new effective temperature (ET)
• predicted mean vote (PMV)
• standard effective temperature (SET)
As described in the Methods Chapter (2), data were binned into half-degree steps for the
thermal indices. Simple linear regression models within each building were fitted to these
binned ASH data as follows:
mean thermal sensation = a + b * (bin index value)
The regression models generated within the SAS® software package weighted each data
point (bin) according to the number of observations it represented; the purpose being to
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 68
minimize the impact of outlying data points that were based on relatively small number of
observations.
The resulting models and their associated statistics have been plotted building-by-building in
Appendix A. Of the 160 separate building analyzed, only results from models that achieved
a statistical significance level (T test) of 95% or better were extracted for use in this
chapter’s meta-analysis.
3.1.1.1. Dependence of thermal sensation on indoor operative temperature
Regression of binned mean thermal sensation (ASH) on indoor operative temperature
(TOP) was performed building-by-building. Individual graphs of the models can be found in
Appendix A. Of the total 160 models fitted, 99 (out of 157 buildings, with 3 missing values)
achieved statistical significance at the 95% confidence level. Table 3.1 below summarises
the significant regression models.
Table 3.1: Summary of the weighted linear regression of bin mean thermal sensation on indoor operative temperature (°C).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings
109 (2 missing values)
44 (1 missing value)
4 (no missing
values) number of buildings with regression models achieving 95% significance
63 (57.8% of total)
36 (81.8% of total)
3 (75.0% of total)
mean (±stdev) model constant (y-intercept)*
-11.96 (±5.839)
-6.65 (±3.572)
-8.65 (±2.982)
mean (±stdev) model gradient* 0.51 (±0.248)
0.27 (±0.134)
0.39 (±0.105)
* Based on those models (y=a + b*TOP) achieving 95% statistical significance or better
The relatively small number of central HVAC buildings listed in Table 3.1 as producing a
significant regression model is probably related to the relatively small number of temperature
bins (independent variable), found within such buildings (i.e. tightly controlled temperatures).
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 69
Naturally ventilated buildings, on the other hand, provided significant regression equations in
four out of every five cases.
The last row of Table 3.1 indicates that occupants of centrally air-conditioned buildings had
thermal sensations that were approximately twice as sensitive to indoor operative
temperatures as those of occupants of naturally ventilated buildings. On average, mean
thermal sensations changed one unit every two degrees of operative temperature in centrally
air-conditioned buildings, whereas in naturally ventilated buildings, a four degree change
was needed to shift mean thermal sensations jump by one unit. Testing this difference in
sensitivity with the T statistic indicated it was significant (T=5.37, df=97, p<0.001).
3.1.1.2. Dependence of thermal sensation on indoor ET
Regression of binned mean thermal sensation (ASH) on indoor new effective temperature
(ET) was performed building-by-building and individual graphs of the models are presented
in Appendix A. Of the total 160 weighted models fitted, 98 (out of 157, minus 3 missing
values) achieved statistical significance at the 95% confidence level. Table 3.2 below
summarises the broad findings.
Table 3.2: Summary of the weighted linear regression of bin mean thermal sensation on indoor effective temperature (°C).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings
109 (2 missing values)
44 (1 missing value)
4
number of buildings with regression models achieving 95% signif.
64
(58.7% of total)
34
(77.3% of total)
3
(75.0% of total) mean (±stdev) model constant (y-intercept)*
-11.81 (±6.383)
-6.76 (±3.076)
-7.94 (±1.884)
mean (±stdev) model gradient *
0.50 (±0.273)
0.28 (±0.126)
0.37 (±0.075)
* Based on those models (y=a + b*ET) achieving 95% statistical significance or better
As was found with the operative temperature index in Table 3.1, thermal sensations were
approximately twice as sensitive to ET in centrally conditioned buildings compared to
naturally ventilated buildings (T=4.45, df=96, p<0.001). This suggests that the difference
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 70
between classes of building was not simply a result of humidity effects being ignored by the
operative temperature index of indoor climate.
3.1.1.3. Dependence of thermal sensation on PMV
Regression of binned mean thermal sensation (ASH) on indoor Predicted Mean Vote
(PMV) was performed building-by-building and individual graphs of the models are
presented in Appendix A. Of the total 160 weighted models fitted, 60 (out of 159, minus 1
missing value) achieved statistical significance at the 95% confidence level. Table 3.3
below summarises the broad findings.
Table 3.3: Summary of the weighted linear regression of bin mean thermal sensation on indoor Predicted Mean Vote (7-pt scale).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings
111 (no missing values)
44 (1 missing value)
4 (no missing values)
number of buildings with regression models achieving 95% signif.
33
(29.7% of total)
28
(63.6% of total)
3
(75.0% of total) mean (±stdev) model constant (y-intercept)*
0.06 (±0.274)
-0.04 (±0.611)
0.68 (±0.697)
mean (±stdev) model gradient*
0.74 (±0.271)
0.62 (±0.329)
0.65 (±0.195)
* Based on those models (y=a + b*PMV) achieving 95% statistical significance or better
Since the units of the PMV index and ASHRAE thermal sensation scale are one and the
same, one would expect the gradient of the regressions models in Table 3.3 above to have
been unity, on average. As indicated by the mean gradients in Table 3.3, however, actual
mean thermal sensations appear to be less sensitive than PMV predicted, although in the
case of centrally conditioned buildings, the observations were about 75% of expectation.
This ratio dropped to about 60% in naturally ventilated buildings, but the difference between
HVAC and NV was not statistically significant (T=1.56, df=59, p>0.1).
The fact that the difference in thermal sensitivity between air-conditioned and naturally
ventilated buildings dropped from the 2:1 ratio that was observed with the simpler thermal
indices of operative and effective temperature suggests that other physical and behavioral
factors were affecting the human body heat balance equation, especially in the naturally
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 71
ventilated context. Factors such as clothing insulation and air speed, that are excluded from
the simpler indices but incorporated in the PMV calculations, might explain some of the
relationship between thermal sensations and indoor temperatures. That is, occupants within
naturally ventilated buildings were more thermally adaptable at manipulating their heat
balance than their counterparts in centrally conditioned buildings.
3.1.1.4. Dependence of thermal sensation on indoor SET
Regression of binned mean thermal sensation (ASH) on indoor standard effective
temperature (SET) was conducted building-by-building across the RP-884 database and
individual graphs of the models are presented in Appendix A. Of the total 160 weighted
models fitted, 56 (out of 152, 8 missing values) achieved statistical significance at the 95%
confidence level. Table 3.4 below summarises the broad findings.
Table 3.4: Summary of the weighted linear regression of bin mean thermal sensation on indoor standard effective temperature (°C).
centrally heated/air-conditioned
buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings
106 (5 missing values)
43 (2 missing values)
3 (1 missing value)
number of buildings with regression models achieving 95% signif
32
(30.2% of total)
27
(62.8% of total)
3
(100% of total) mean (±stdev) model constant (y-intercept)*
-5.06 (±3.42)
-4.40 (±2.04)
-4.66 (±2.23)
mean (±stdev) model gradient*
0.21 (±0.14)
0.18 (±0.08)
0.21 (±0.06)
* Based on those models (y=a + b*SET) achieving 95% statistical significance or better
Table 3.4 indicates that regression gradients were relatively constant across all classes of
building at about one thermal sensation unit for each five degrees of environmental
temperature. The same interpretation that we applied to the PMV index (preceding section)
is relevant here as well -- namely that heat balance factors included in these more complex
indices such as PMV and SET can account for the greater degree of thermal adaptability in
naturally ventilated buildings compared to centrally conditioned buildings.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 72
3.1.2. Thermal neutrality
The term “thermal neutrality” refers to a specific value of the indoor thermal environmental
index (e.g. operative temperature) corresponding to a mean thermal sensation vote of zero
on the seven-pt scale (i.e.“neutral”). Neutrality is readily obtained by solving each building’s
regression equation for y=0.
3.1.2.1. Neutral operative temperatures (neut_top)
Solution of the regression equations for the “neutral” sensation in relation to the indoor
operative temperature (top) was performed building-by-building. Table 3.5 below
summarises the neut_top statistics from 160 buildings.
Table 3.5: Summary of the neutral operative temperatures (neut_top) from 160 buildings in the database (°C).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of signif results in summer building sample*
47 out of 78
(I missing value)
27 out of 32
(1 missing value)
1 out of 2
(no missing values)
mean neut_top (±stdev) in summer sample °C
24.1 (±1.31)
24.6 (±2.42)
23.9 (±0)
number of signif results in winter building sample*
14 out of 30 (2 missing values)
7 out of 11 (1 missing value)
2 out of 2 (no missing values)
mean neut_top (±stdev) in the winter sample °C
22.5 (±0.35)
22.4 (±2.78)
20.7 (±0.50)
* only results from buildings with statistically significant regression models used
Table 3.5 indicates that thermal neutrality was observed across the winter experiments in
RP-884 database at an average indoor operative temperature of about 22.5°C, regardless
of whether the buildings were centrally conditioned or naturally ventilated. However, the
standard deviation of winter neutralities in the sample of naturally ventilated buildings was
about eight times that observed in the centrally conditioned buildings. In the summer field
experiments there was a tendency for neutrality to be half a degree warmer in naturally
ventilated buildings compared to centrally conditioned, but the difference failed to reach
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 73
significance (T = 1.16, df = 72, p > 0.1). The standard deviation of neutralities was again
found to be larger in the summer sample of naturally ventilated buildings.
All Buildings
15
17
19
21
23
25
27
29
18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
neut
ral o
pera
tive
tem
pera
ture
(oC
)
neut_top = 15.34 + 0.35 * top
R2 = 0.38, p = 0.0001
Central HVAC and Mixed Mode Buildings
15
17
19
21
23
25
27
29
18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
neut
ral o
pera
tive
tem
pera
ture
(oC
)
neut_top = 8.92 + 0.62 * top
R2 = 0.27, p = 0.0001
Naturally Ventilated Buildings
15
17
19
21
23
25
27
29
18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
neut
ral o
pera
tive
tem
pera
ture
(oC
)
neut_top = 15.47 + 0.35 * top
R2 = 0.32, p = 0.0013
Figure 3.1: Dependence of neutral indoor temperature on buildings’ mean temperature
The adaptive hypothesis predicts that the temperatures regarded as “neutral” within any
particular building will depend, in part, on the level of warmth typically encountered and
expected within that building. Figure 3.1 expresses this idea statistically by regressing the
neut_top observations against mean indoor operative temperatures. Figure 3.1 lends
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 74
support to this adaptive hypothesis insofar as the models show a moderate linear correlation
(r=0.5~0.6). The main regression equation (“all buildings” in Figure 3.1) indicates that
indoor neutrality increases by about one degree (°C) for every three degrees increase of
indoor temperature. Despite the apparent difference in gradients between the HVAC and
NV building samples, their regression gradients were not significantly different (T=0.08,
df=96, p>0.5).
3.1.2.2. Neutral effective temperatures (neut_et)
Solution of the regression equations for the “neutral” sensation in relation to ET was
performed building-by-building across the RP-884 database. Table 3.6 below summarises
the neut_et findings from 160 buildings. Effective temperature neutralities within the
naturally ventilated buildings were, on average, about half a degree warmer than their
centrally conditioned counterparts in both seasons, but the difference was insignificant
(summer T = 1.12, df = 70, p > 0.2 and winter T = 0.99, df = 21, p > 0.2).
Table 3.6: Summary of the neutral effective temperatures (neut_et) from 160 buildings in the database (°C).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings in summer sample*
45 out of 78
(1 missing value)
27 out of 31
(2 missing values)
1 out of 2
(no missing values)
mean neut_et (±stdev) in summer sample °C
24.0 (±1.63)
24.6 (±2.91)
23.4 (±0)
number of buildings in winter sample*
16 out of 30 (2 missing values)
7 out of 11 (1 missing value)
2 out of 2 (no missing values)
mean neut_et (±stdev) in the winter sample °C
22.4 (±0.77)
22.9 (±1.68)
20.7 (±0.55)
* only results from buildings with statistically significant regression models used
3.1.2.3. Neutral predicted mean votes (neut_pmv)
Solution of the regression equations for the “neutral” sensation in relation to the PMV index
was done building-by-building. Since both dependent and independent variables are based
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 75
on the same scales, the static heat-balance model theory predicts that subjects would vote
zero (neutral) when they were in heat-balance conditions corresponding to PMV=0.
However, results of these regression analyses indicate significant departures from this
expectation. Table 3.7 below summarizes the neut_pmv findings from 160 buildings in the
RP-884 meta-analysis. Many buildings in the database failed to produce statistically
significant models when using the PMV index, but those that did indicated that occupants of
HVAC buildings in summer found neutrality in indoor climatic conditions that corresponded
with PMV=0; i.e. model matched observation quite closely. On the other hand, occupants of
naturally ventilated buildings during summer found themselves feeling neutral in indoor
conditions that the PMV model indicated as cooler-than-neutral. This HVAC v NV difference
however, proved to be statistically insignificant (T = 1.48, df = 47, p > 0.1) due to the small
number of buildings being compared.
Table 3.7: Summary of the neutral Predicted Mean Votes neut_pmv from 160 buildings in the database (7-pt scale).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings in summer sample*
23 out of 79
(no missing values)
26 out of 32 (1 missing)
1out of 2
(no missing values)
mean neut_pmv (±stdev) in the summer sample
0.01
(±0.32)
-0.43
(±1.40)
-0.24±0)
number of buildings in winter sample*
7 out of 32
(no missing values)
1 out of 12
(1 missing value)
1 out of 2
(no missing values) mean neut_pmv (±stdev) in the winter sample
-0.55
(±0.301)
1.11 (±0)
-0.53 (±0)
* only results from buildings with statistically significant regression models used
3.1.2.4. Predicted neutralities with the PMV heat balance model
As indicated in the preceding table, neutrality in several buildings occurred in thermal
environmental conditions that departed significantly from the predictions of the static heat-
balance models of thermal comfort. Another way of examining this issue is to use the PMV
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 76
model to actually predict the neutral operative temperature for each building, ceteris paribus.
This new variable in the meta-analysis was code-named PREDNEUT.
All buildings
10
12
14
16
18
20
22
24
26
28
30
12 14 16 18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
pre
dn
eu
t (o C
)
predneut = 15.43 + 0.33 * top
R2 = 0.54, p = 0.0001
Central HVAC and Mixed Mode buildings
10
121416
1820
2224
262830
12 14 16 18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
pre
dn
eu
t (o C
)
predneut = 13.16 + 0.43 * top
R2 = 0.15, p = 0.0001
Naturally Ventilated buildings
10
1214
16
1820
22
2426
28
30
12 14 16 18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
pre
dn
eu
t (o C
)
predneut = 15.07 + 0.34 * topR2 = 0.70, p = 0.0001
Figure 3.2: Dependence of neutrality predicted by the PMV heat-balance model (PREDNEUT) on mean indoor temperatures (TOP).
The clear dependence of predneut on mean indoor top in Figure 3.2 suggests that the other
heat balance variables that change in response to indoor temperature -- such as clothing
insulation and air speeds, were driving the PMV predicted neutralities. The correlation
appears to be strongest in the case of the naturally ventilated buildings (r=0.84). In the case
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 77
of the HVAC and mixed mode buildings in Figure 3.2, the seven outlying buildings below the
fitted regression line were from the Brown (1992/3) study in industrial settings where
metabolic rates were elevated well above those encountered in the remaining office and
residential buildings, causing the predicted neutrality to be depressed by as much as 10°C
below the trendline. These outliers account for the reduction in correlation coefficients.
3.1.2.5. Neutral standard effective temperatures (neut_set)
Solution of the regression equations for the “neutral” sensation in relation to SET was
performed building-by-building. Table 3.8 below summarises the neut_set findings from
160 buildings.
Table 3.8: Summary of the neutral standard effective temperatures (neut_set) (°C).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings in summer sample*
24 out of 73
(6 missing values)
25 out of 31
(2 missing values)
1 out of 1
(1 missing value)
mean neut_set (±stdev) in the summer sample
24.5
(±1.51)
24.1
(±2.85)
23.8 (±0)
number of buildings in winter sample*
8 out of 30
(2 missing values)
2 out of 11
(1 missing value)
2 out of 2
(no missing values)
mean neut_set (±stdev) in the winter sample
25.2
(±3.51)
31.3
(±1.89)
19.6
(±6.73)
* only results from buildings with statistically significant regression models used
Less than one third of the centrally heated/air-conditioned buildings in the sample yielded
significant regression models, probably because of the restricted range of thermal
environmental conditions in such buildings. Naturally ventilated buildings, with their greater
internal climatic variety produced a significant regression equation against the SET index in
the majority of cases. In those buildings sampled during the summer season, there was an
average neutral SET in the 24~24.5°C region and the difference of 0.4 K between the
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 78
means of naturally ventilated and centrally heated/air-conditioned buildings was not
statistically significant (T = 0.61, df = 47, p > 0.5). The anomalously high average neutral
SET of 31.3°C observed in naturally ventilated buildings in winter was based on only two
buildings. The seasonal difference in neutrality for HVAC buildings was not statistically
significant (T = 0.8, df = 30, p > 0.2).
3.1.3. Thermal acceptability and indoor climate
Thermal acceptability was directly assessed in a small subset of studies in the RP-884
database, but it could be inferred from thermal sensation votes, which were recorded in all
studies. This section analyzes both direct and inferred versions of thermal acceptability
response.
3.1.3.1. Relationship between direct and inferred thermal acceptability
Testing the assumption that a thermal sensation vote falling in the interval -1.5<ASH<+1.5
equated with thermal acceptability was possible by comparing frequencies of both direct
and indirect thermally acceptable votes within each building. Each building’s frequency of
directly assessed acceptable votes was coded as f_tsa_2 and the frequency of acceptable
thermal sensations was coded as prxy_tsa in the meta-analysis.
The result have be depicted in Figure 3.3. The graphs represent weighted regression
models of prxy_tsa versus f_tsa_2. Each point in the graphs represents a specific building
in the database. The solid line plotted through the data points represents the expected
relationship (gradient 1:1) whereas the dotted line represents the line of best fit (with model
equation and statistics annotated on each graph).
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 79
Townsville Australia RP-702 (tropical dry season), HVAC buildings.
prxy_tsa = 0.55 * tsa + 38.849
R2 = 0.4685
60
70
80
90
100
60 70 80 90 100
TSA (% aceptable)
Pro
xy T
SA
(%
"sa
tisfa
ctor
y" A
SH
vot
es)
Townsville Australia RP-702(tropical wet season), HVAC buildings.
prxy_tsa = 0.4288 * tsa + 46.302
R2 = 0.2038
60
70
80
90
100
60 70 80 90 100
TSA (% acceptable)
Pro
xy T
SA
(%
"sa
tisfa
ctor
y" A
SH
vot
es)
Montreal Canada RP-821 (summer), HVAC buildings.
prxy_tsa = 0.5007 * tsa + 32.536
R2 = 0.4752
50
60
70
80
90
100
50 60 70 80 90 100
TSA (% acceptable)
Pro
xy T
SA
(%
"sa
tisfa
ctor
y" A
SH
vot
es)
Montreal Canada RP-821 (winter), HVAC buildings.
prxy_tsa = 1.3448 * tsa - 35.093
R2 = 0.7155
50
60
70
80
90
100
50 60 70 80 90 100
TSA (% acceptable)
Pro
xy T
SA
(%
"sa
tisfa
ctor
y" A
SH
vot
es)
Sydney Australia (summer and winter) HVAC and Mixed buildings.
prxy_tsa = -0.2419 * tsa + 92.27
R2 = 0.9872
60
70
80
90
100
60 70 80 90 100
TSA (% acceptable)
Pro
xy T
SA
(%
"sa
tisfa
ctor
y" A
SH
vot
es)
proxy acceptability versus actual acceptability for all available buildings
30
40
50
60
70
80
90
100
30 40 50 60 70 80 90 100
TSA (% acceptable)
Pro
xy T
SA
(% "
satis
fact
ory"
AS
H v
otes
)
prxy_tsa = 30.74 + 0.61 * tsa
R2 = 0.48, p = 0.0001
Figure 3.3: Comparison of directly determined and inferred thermal acceptability. Each data point represents an individual building from the RP-884 database.
Figure 3.3 indicates that the strength of association between direct and inferred thermal
acceptability varied considerably across field experiments. For the pooled analysis of all
seven experiments’ buildings (top left panel in Figure 3.3), about half of the variance in
acceptable thermal sensations (prxy_tsa) could be accounted for by the direct thermal
acceptability ratings. Discounting Rowe’s (1996) Sydney experiment due to its small
sample size (three building data points), the highest correlation was found in Donnini’s
(1996) Montreal winter experiment (r = 0.85). For the remaining experiments, the
correlations can be described as moderate. In five out of the seven individual project graphs
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 80
depicted in Figure 3.3, the gradient of the observed dependence of inferred acceptability on
directly stated acceptability was significantly lower than the unity we expected. Subjects
apparently were voting that thermal sensations outside the central three categories of the
ASHRAE 7-pt scale were still acceptable. Expressing that a different way, buildings that
had a high rating of thermal acceptability on the direct scale (>80%), typically scored lower
acceptability ratings on the basis of percentage of thermal sensations falling within the
central three categories of the 7-pt scale.
3.1.3.2. Directly determined thermal acceptability.
As noted in the preceding section, “acceptable” TSA votes (“At the present time, is this
thermal environment acceptable to you or not?”) were tallied for each building and
expressed as a percentage of all responses in the building. This percentage was coded as
f_tsa_2 for each building in the RP-884 meta-analysis.
Ignoring the upper and lower humidity boundaries of the summer and winter comfort zones
depicted in ASHRAE Standard 55-92, the percentage of indoor climate measurements
within each RP-884 database building complying with the relevant summer or winter
ASHRAE comfort zone ET boundaries was coded as ASH55_92 in the meta-analysis. A
simple assessment of the practical utility of the ASHRAE comfort zones can be performed
by comparing these acceptability levels predicted from indoor climatic measurements
(ASH55_92) with the corresponding thermal acceptability ratings for each building. These
comparisons have been performed in Figures 3.4 and 3.5 respectively -- each data point in
the graphs represents a single building.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 81
Thermal Acceptability (all buildings)
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
% indoor climates falling within ASHRAE 55-92 comfort zones
TS
A (
% a
ccep
tabl
e)
tsa = 75.05 + 0.03 * ASH55_92
R2 = 0.01, p = 0.5258
Figure 3.4: Relationship between direct thermal acceptability ratings of buildings (f_tsa_2) and buildings’ compliance with ASHRAE Standard 55-1992 comfort zone ET boundaries (ASH55_92).
Thermal Acceptability (all buildings)
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
% indoor climates falling within ASHRAE 55-92 comfort zones
Pro
xy T
SA
(%
vot
es -
1.5
< a
shra
e <
1.5
)
prxy_tsa = 70.96 + 0.16*ASH55_92
R2 = 0.14, p = 0.0001
Figure 3.5: Relationship between acceptability thermal sensation ratings of buildings (prxy-tsa) and buildings’ compliance with ASHRAE Standard 55-1992 comfort zone ET boundaries (ASH55_92).
Regardless of which thermal acceptability measure was adopted, Figures 3.4 and 3.5
indicate that compliance with the ET prescriptions of ASHRAE Standard 55-1992 had
little or no bearing on the buildings’ acceptability ratings by occupants. This is indicated
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 82
clearly by the complete lack of statistical significance in the regression models plotted on the
graphs in Figures 3.4 and 3.5. A logical extension of this null result is that most of the
buildings which had very low levels of compliance with ASHRAE 55-92 (say, ASH55_92 <
30%) still had occupant ratings of thermal acceptability better than 60 to 70%
TSA versus TOP for all buildings
0
20
40
60
80
100
18 20 22 24 26 28 30 32 34
mean indoor operative temperature (oC)
TSA
(% a
ccep
tabl
e)
tsa = -287.56 + 27.75 * top - 0.52 * top2
R2 = 0.08, p = 0.0883
TSA versus ET for all buildings
0
20
40
60
80
100
18 20 22 24 26 28 30 32 34
mean indoor effective temperature (oC)
TSA
(% a
ccep
tabl
e)
tsa = -253.80 + 25.64 * et - 0.49 * et 2
R2 = 0.06, p = 0.1554
TSA versus SET for all buildings
0
20
40
60
80
100
18 20 22 24 26 28 30
mean indoor standard effective temperature (oC)
TSA
(% a
ccep
tabl
e)
tsa = -724.32 + 61.02 * set - 1.16 * set 2
R 2 = 0.22, p = 0.0005
TSA versus PMV for all buildings
0
20
40
60
80
100
-3 -2 -1 0 1 2 3
mean indoor predicted mean vote
TSA
(% a
ccep
tabl
e)
tsa = 77.23 + 16.00 * pmv - 11.63 * pmv2
R2 = 0.18, p = 0.0030
Figure 3.6: Dependence of direct thermal acceptability ratings on mean thermal index values. Each data point represents a building.
Figure 3.6 shows the percentage of occupants within each building voting “acceptable”
(f_tsa_2) as a function of the mean indoor climatic index values recorded for each building.
The indices selected for this analysis covered the spectrum from relatively simple operative
temperature up to fully developed heat balance indices such as PMV and SET. The
expected relationship between percentage satisfied and indoor warmth is hyperbolic,
peaking around the database’s mean neutrality or preferred temperature. Unfortunately the
majority of buildings available for the analysis were clustered within a fairly narrow band of
indoor temperatures, centred on 23°C, and so the data are not well suited to regression
analysis. As a result, the weighted 2nd order polynomial models fitted to the TOP and ET
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 83
indices in Figure 3.6 were not statistically significant. While the models fitted for the more
sophisticated heat balance models such as PMV and SET did achieve statistical
significance, the explained variance was about 20% in both cases.
The underlying concept of Fanger’s Predicted Percentage Dissatisfied index (1970) is
simply that as mean indoor climatic conditions depart from the optimum (assumed to be
PMV=0), the percentage of persons experiencing unacceptable thermal sensations
increases. Despite the obvious lack of normality in statistical distributions for TSA % across
the RP-884 building database, the 2nd order polynomial regression equation fitted to mean
building PMV values in Figure 3.6 is of the hyperbolic form suggested by the PPD concept.
The fact that the PMV index produced a statistically significant relationship (R2= 0.18) where
the simpler indices of TOP and ET failed suggests that the inclusion of other heat balance
factors such as air speed, metabolic rate and clothing actually does what it’s supposed to do
-- improve predictions. The same interpretation can be applied to the SET index, since it
too incorporates the full array of heat balance variables, and as seen in Figure 3.6, its 2nd
order polynomial model was also statistically significant (p=0.0005).
3.1.3.3. Thermal acceptability inferred from thermal sensation.
Fanger’s Predicted Percentage Dissatisfied (PPD) model is premised on the assumption
that a thermal sensation vote within the central three categories of the ASHRAE 7-point
scale (slightly cool + neutral + slightly warm) is acceptable and satisfactory. Since it is
derived from the core thermal response item of ASH, this proxy for thermal acceptability was
obtained for every respondent in the cumulative ASHRAE RP-884 database (n>21,000) and
coded as prxy_tsa.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 84
Proxy TSA versus TOP for all buildings
0
20
40
60
80
100
10 15 20 25 30 35
mean indoor operative temperature (oC)
Pro
xy T
SA
(% a
ccep
tabl
e)
prxy_tsa = -176.48 +20.56 * top - 0.41 * top2
R2 = 0.49, p = 0.0001
Proxy TSA versus ET for all buildings
0
20
40
60
80
100
10 15 20 25 30 35
mean indoor effective temperature (oC)
Pro
xy T
SA
(% a
ccep
tabl
e)
prxy_tsa = -171.19 + 20.21 * et - 0.40 * et 2
R2 = 0.51, p = 0.0001
Proxy TSA versus SET for all buildings
0
20
40
60
80
100
14 16 18 20 22 24 26 28 30 32 34
mean indoor standard effective temperature (oC)
Pro
xy T
SA
(% a
ccep
tabl
e)
prxy_tsa = -679.20 + 56.70 * set - 1.05 * set 2
R 2 = 0.41, p = 0.0001
Proxy TSA versus PMV for all buildings
0
20
40
60
80
100
-3 -2 -1 0 1 2 3
mean indoor predicted mean vote
Pro
xy T
SA
(% a
ccep
tabl
e)
prxy_tsa = 83.72 + 10.75 * pmv - 10.52 * pmv2
R2 = 0.42, p = 0.0001
Figure 3.7: Dependence of building acceptability ratings (derived from thermal sensation) on mean thermal index values. Each data point represents a building.
As noted in the preceding section, the majority of buildings in the RP-884 database were
clustered within a narrow range of mean indoor temperatures, severely limiting the scope for
regression analyses. However in Figure 3.7, because of the larger sample size compared
with the preceding section, all four indoor climatic indices showed statistically significant
relationships with this proxy building thermal acceptability index.
3.1.3.4. Thermal sensitivity and the range of thermally acceptable temperatures.
Given the relatively weak correlations for thermal acceptability in the preceding sections, the
use of the associated regression models to define acceptable ranges of thermal indices
would be not very reliable. A more feasible alternative, based on Fanger’s Predicted
Percentage Dissatisfied (PPD) concept (1970), can be applied to this question of
acceptable ranges. As noted earlier, PPD is a function of mean thermal sensation (PMV in
Fanger’s terminology) and a PMV of ±0.85 is assumed to correspond with 80%
acceptability. Logically therefore, assuming that actual thermal sensation votes (ASH) are
distributed around their mean with a similar variance as predicted votes are (PMV/PPD),
the values of a particular indoor thermal index (e.g. TOP, ET, PMV or SET) corresponding
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 85
with mean ASH votes of ±0.85 can be interpreted as the limits of acceptable thermal
environments (for 80% acceptability). This derivation of acceptable ranges was
operationalized by solving the ASH linear regression models (Appendix A) that we defined
for each of the main indoor thermal indices (TOP, ET, PMV, SET) for each of the buildings
in the RP-884 database, using ASH=-0.85 and again using ASH=+0.85. Subtraction of the
index value, say TOP, corresponding with -0.85 from the corresponding +0.85 value defines
the width of 80% acceptable TOP values for that particular building and the variable thus
defined was codenamed RANG_TOP in the meta-analysis.
Note that acceptable temperature ranges using these techniques were only feasible in
buildings whose ASH regression models (Appendix A) achieved statistical significance at
the 95% confidence level.
Table 3.9: Range of acceptable operative temperatures (Kelvin).
centrally heated/air-conditioned
buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings 108 (3 missing values)
41 (4 missing values)
4 (no missing values)
number of buildings with regression models achieving 95% significance*
62
(57% of total)
33
(75% of total)
3
(75% of total)
80% acceptability criterion (RANG_TOP) Mean (±stdev)
4.1
(±1.91)
6.9
(±2.79)
4.5
(±1.24) 90% acceptability criterion (RANTOP10) Mean (±stdev)
2.4
(±1.12)
4.9
(±3.27)
2.7
(±0.73)
* Based on those thermal sensation models in Appendix A (y=a + b*TOP) achieving 95% statistical significance or better
The 80% acceptable range of operative temperatures was, on average, 6.9 K wide in
naturally ventilated buildings, which was about 70% wider than in centrally heated/air-
conditioned buildings. This difference was statistically significant (T = 5.69, df = 93,
p<0.001). The acceptable range of operative temperatures for mixed mode buildings was,
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 86
on average, between that of HVAC and NV buildings, but the small number of cases
precludes any statistical tests.
Also included in Table 3.9 are the acceptable operative temperature ranges for a more
stringent criterion of 90% acceptability (labelled RANTOP10 in the meta-anlysis). These
were derived from each building’s thermal sensation v operative temperature regression
equation, but instead of solving for neutrality ± 0.85 sensation units (as was the case for the
80% criterion used for RANG_TOP), we applied the PPD=10% assumption, namely
neutrality ± 0.5 sensation units. The acceptable ranges in Table 3.9 reduced from 4.1 K for
80% acceptability in HVAC buildings to 2.4 K using the 90% acceptability criterion (not
dissimilar to the prescriptive ranges found in ASHRAE Standard 55-92 for the same
acceptability criterion for general thermal comfort, excluding local discomforts). However,
the average 90% acceptability range observed for RP-884’s naturally ventilated sample was
twice as wide as observed in the HVAC sample in Table 3.9 (and also prescribed in
ASHRAE Standard 55-92).
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 87
All Buildings
0
2
4
6
8
10
12
14
17 19 21 23 25 27 29 31 33
mean indoor operative temperature (oC)
ran
g_
top
(K
)
rang_top = 61.87 - 4.58 * top + 0.09 * top2
R2 = 0.18, p = 0.0001
Naturally Ventilated buildings
0
2
4
6
8
10
12
14
17 19 21 23 25 27 29 31 33mean indoor operative temperature (oC)
ran
g_
top
(K
)
rang_top = 30.51 - 1.82 * top + 0.04 * top2
R2 = 0.02, p = 0.7189
Central HVAC and Mixed Mode buildings
0
2
4
6
8
10
12
14
17 19 21 23 25 27 29 31 33
mean indoor operative temperature (oC)
ran
g_
top
(K
)
rang_top = 145.35 - 11.85 * top + 0.25 * top2
R2 = 0.14, p = 0.0099
Figure 3.8: Dependence of the acceptable range of operative temperature within buildings on mean operative temperature indoors
Figure 3.8 indicates a loose relationship between acceptable ranges and mean indoor
operative temperatures (RANG_TOP) -- as a building’s mean indoor temperature deviates
from moderate levels in the vicinity of 24~25°C, the acceptable range tends to increase.
Conducting this analysis separately on the HVAC and NV buildings indicates a lack of any
statistical relationship for the sample of naturally ventilated buildings.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 88
Central HVAC and Mixed Mode buildings
0
2
4
6
8
10
12
14
0 1 2 3 4 5
stdev. indoor operative temperature (oC)
ran
g_
top
(K
)
rang_top = 3.79 + 0.57 * stdev_ topR2 = 0.06, p =0.0580
Naturally Ventilated buildings
0
2
4
6
8
10
12
14
0 1 2 3 4 5stdev. indoor operative temperature (oC)
ran
g_
top
(K
)
rang_top = 4.16 + 1.65 * stdev_ top
R2 = 0.26, p = 0.0034
All Buildings
0
2
4
6
8
10
12
14
0 1 2 3 4 5
stdev. mean indoor operative temperature (oC)
ran
g_
top
(K
)
rang_top = 3.19 + 1.82 * stdev. topR2 = 0.35, p = 0.0001
Figure 3.9: Dependence of the acceptable range of operative temperatures (TOP) within buildings on their standard deviation of operative temperature indoors
The adaptive hypothesis emphasises the effects of expectation on thermal acceptability. If a
particular building’s indoor climate is characterized by large variations in temperature, both
temporally and spatially, the adaptive hypothesis predicts a corresponding widening in the
range of indoor temperatures considered acceptable by its occupants. Figure 3.9 depicts
the linear relationship between the range of acceptable operative temperatures and the
standard deviation of indoor operative temperature. The model was statistically significant
with a correlation coefficient r = +0.59, and the regression equation indicates that the
acceptable range (-0.85< ASH < +0.85) increases by about two degrees for a single degree
increase in standard deviation of operative temperature. So, in tightly controlled HVAC
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 89
buildings depicted in Figure 3.9 where we find relatively small standard deviations of
operative temperature, there is a clear trend for the gradient of ASH v TOP regression
models to increase (see Appendix A). Consequently the range of acceptable temperatures
appears to be much greater in naturally ventilated buildings where thermal variability is the
norm compared to HVAC buildings.
3.1.4. Thermal preferences and indoor climate
One hundred and sixteen of the 160 buildings in ASHRAE RP-884’s database assessed
thermal preferences with a questionnaire item along these lines:
“At this point in time, would you prefer to feel warmer, cooler, or no change?”
Probit regression analysis (Finney, 1971; Ballantyne, 1977) rather than linear regression has
been separately applied to the votes for warmer and cooler conditions for each building.
Preferred temperature (of whatever index) was defined as that value of the independent
variable (thermal index) corresponding to the intersection of the “want cooler” and “want
warmer” probit models (see Appendix B). Table 3.10 below summarises the main statistics
for preferred operative temperatures for the 116 buildings in which the questionnaire item
was available.
Table 3.10: Summary of the preferred operative temperatures (preftemp) (°C).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings in summer sample*
62 (17 missing values)
24 (9 missing values)
1 (1 missing value)
mean preftemp (±stdev) in the summer sample
23.1 (±1.26)
24.3 (±2.13)
24 (±0)
number of buildings in winter sample*
22 (10 missing values)
6 (6 missing values)
1 (1 missing value)
mean preftemp (±stdev) in the winter sample
22.9 (±1.19)
23.1 (±1.61)
21.7 (±0)
* results not based on statistically significant regression models.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 90
The results in Table 3.10 indicate a fairly constant temperature preference of about 23°C in
centrally controlled HVAC buildings. Winter temperature preferences in naturally ventilated
buildings were on average about a degree cooler than summer preferences, but this failed
to meet statistical significance, due to the small sizes and large standard deviations. The
summer temperature preferences in HVAC buildings were on average about one degree
cooler than those in naturally ventilated buildings and because of the more substantial
sample sizes in this season, the difference was significant (T = 3.23, df = 84, p < 0.002).
The trivial difference in temperature preferences between HVAC and NV buildings in winter
at less than a third of a degree was statistically insignificant (T = 0.34, df = 26, p > 0.5).
All Buildings
17
19
21
23
25
27
29
17 19 21 23 25 27 29 31 33 35
mean indoor effective temperature (oC)
pref
erre
d op
erat
ive
tem
pera
ture
(oC
)
preftemp = 16.21 + 0.30 * et
R2 = 0.30, p 0.0001
All Buildings
17
19
21
23
25
27
29
18 20 22 24 26 28 30 32 34
mean indoor operative temperature (oC)
pref
erre
d op
erat
ive
tem
pera
ture
(oC
)
preftemp = 16.30 + 0.29 * top
R2 = 0.28, p = 0.0001
All Buildings
17
19
21
23
25
27
29
-1 -0.5 0 0.5 1 1.5 2 2.5
Predicted Mean Vote
pref
erre
d op
erat
ive
tem
pera
ture
(o C
)
preftemp = 23.15 + 1.30 * pmvR2 = 0.30, p = 0.0001
All Buildings
17
19
21
23
25
27
29
22 24 26 28 30 32 34
mean indoor standard effective temperature ( oC)
pref
erre
d op
erat
ive
tem
pera
ture
(oC
)
preftemp = 12.93 + 0.41* set
R2 = 0.29, p 0.0001
Figure 3.10 Thermal preferences as a function of mean indoor thermal index values (TOP, ET, PMV, SET). Each data point represents a single building.
Figure 3.10 indicates that the operative temperature preferred by building occupants was
moderately correlated with mean levels of warmth prevailing within their buildings at the time
of the field survey. The strength of correlation was reasonably consistent at about r=+0.55
across all four indoor climatic indices (TOP, ET, PMV and SET).
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 91
3.1.5. Comparisons between neutral and preferred temperatures indoors.
With various aspects of perceived indoor climates being assessed with different
questionnaire items, there is a possibility that the indoor temperatures defined as optimal
for a particular building and climatic context may in fact vary, depending on whether one is
talking in terms of thermal sensation (neutrality), thermal acceptability (satisfaction) or
thermal preference (preferred temperatures). Indeed, some authors (McIntyre, 1978; de
Dear, 1991c) have suggested that at least some of the statistical dependence of neutrality
on prevailing outdoor climates observed by the pioneers of adaptive models (Auliciems and
Humphreys) may in fact be due to a semantic artefact in the ASHRAE (or Bedford) 7-pt
scale of thermal sensation. Persons living in cold climates may in fact describe their
preferred thermal environment with words like “warm and cosy” while for persons in hot
climates, words like “cool and fresh” may connote their thermal ideal.
The RP-884 database contains 55 buildings in which both thermal sensations (ASH) and
thermal preferences were registered, and so each of these buildings had both a neutrality
and a preferred temperature available in the meta-analysis. A new variable called “semantic
discrepancy” (discrep) was calculated as neutrality minus preferred temperature and
expressed in degrees (°C).
Table 3.11: Statistics for the semantic discrepancy (discrep) between observed neutrality (neut_top) and observed temperature preference (preftemp) (°C).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings in summer sample*
43 out of 62
(17 missing values)
23 out of 24
(9 missing values)
1 out of 1
(1 missing value)
mean discrep (±stdev) in the summer sample
0.7
(±0.78)
0.2
(±1.38)
-0.14 (±0)
number of buildings in winter sample*
13 out of 22
(10 missing values)
6 out of 6
(no missing values)
1 out of 1
(1 missing values)
mean discrep (±stdev) in the winter sample
0.0
(±0.45)
0.3
(±1.00)
-0.7 (±0)
* only results from buildings with statistically significant regression models (neut_top) and probit analyses (preftemp) were used to define discrep
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 92
Table 3.11 indicates that in both seasons and in HVAC and NV buildings alike, the average
semantic discrepancy between neutrality and preference was greater than or equal to zero
degrees. Although the seasonal difference between mean DISCREP in NV buildings was
negligible (T = 0.15, df = 27, p > 0.5), the seasonal difference in HVAC buildings was
statistically significant (T = 2.93, df = 54, p < 0.01). Neither the summer nor the winter
differences between DISCREP in HVAC and NV buildings were significant (T = 1.96, df =
64, p > 0.05 and T = 0.73, df = 17, p > 0.2 respectively).
Figure 3.11 below was designed to test the hypothesis that warm environments promote
positive semantic discrepancies between thermal sensations and preferences, while cool
environments promote negative discrepancies. The seemingly random distribution of data
points in the graph and statistically insignificant correlation and regression in the “all
buildings” panel of Figure 3.11 suggest that mean indoor climatic warmth (top) appears to
exert no systematic influence on the semantics of thermal sensation scales.
Pursuing the semantic artefact hypothesis a little further, the database was disaggregated
into HVAC and naturally ventilated buildings. The lower panels of Figure 3.11 indicates
again that, for the naturally ventilated buildings at least, the mean levels of warmth indoors
had no systematic effect on DISCREP. However, there was a positive, albeit modest,
relationship between the DISCREP variable and mean indoor operative temperature in
HVAC buildings. The gradient on that regression model indicates that, on average,
neutrality inside a centrally air-conditioned building becomes elevated above preferred
temperature by about one degree for every two degrees the mean indoor operative
temperature increases between 21 and 26°C. That is, persons living and/or working in
generally warm centrally air-conditioned buildings seem to be describing their preferred
indoor climate with terms like “slightly cool” while persons in generally cool centrally air-
conditioned buildings seem inclined to describe their preferred indoor climate as “slightly
warm.”
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 93
All Buildings
-4
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32 34
mean indoor operative temperature (oC)
dis
cre
p (o
C)
discrep = 0.12 + 0.01 * top
R2 = 0.002, p = 0.7034
Central HVAC and Mixed Mode buildings
-4
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32 34
mean indoor operative temperature (oC)
dis
cre
p (
o C)
discrep = -12.12 + 0.54 * top
R2 0.25, p = 0.0001
Naturally Ventilated Buildings
-4
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32 34
mean indoor operative temperature (oC)
dis
cre
p (
oC
)
discrep = 0.08 + 0.01 * top
R2 = 0.001, p = 0.8450
Figure 3.11: Dependence of discrep on mean indoor operative temperatures
3.1.6. Behavioural adjustments to indoor climate
As noted in the introductory chapter to this monograph, behavioral thermoregulation involves
a variety of purposive actions that modify the heat and mass exchanges that define the
body’s heat balance with its thermal environment. The most obvious behavioural response
for which we have quantitative data in the RP-884 database is that of clothing insulation.
The other “personal” or behavioral parameter governing the human body’s heat balance for
which we have quantitative estimates in the RP-884 database is metabolic heat. Thirdly,
indoor air speeds which were measured throughout the RP-884 database, is another
parameter over which building occupants exert some behavioral control, either by
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 94
opening/closing windows, or turning on/off fans and similar devices. The following sections
examine these data and their relationships with various indices of indoor climate.
3.1.6.1. Thermal insulation adjustments indoors
Clothing insulation and also the incremental insulation of the chairs upon which the subjects
were sitting at the time of their questionnaire response were converted into clo units
according to the ASHRAE Standard 55 1992 methods. Table 3.12 summarises the main
statistics.
Table 3.12: Statistics for the thermal insulation variable (clothes + furniture) (clo).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings in summer sample*
79
33
2
mean INSUL (±stdev) in the summer sample
0.70
(± 0.077)
0.66
(± 0.125)
0.71
(± 0.008) number of buildings in winter sample*
32
12
2
mean INSUL (±stdev) in the winter sample
0.92 (± 0.126)
0.93 (± 0.331)
0.83 (± 0.259)
Table 3.12 indicates significant seasonal differences in thermal insulation, with average
winter values exceeding 0.9 clo and average summer values around 0.7 clo (T=11.2,
df=109, p<0.001 for HVAC buildings; T=4.0, df=43, p<0.001 for NV buildings). While
seasonal differences were significant, the trivial differences between HVAC and NV sample
means failed to reach statistical significance in either season. However, building mean
insulation values showed greater variability in the naturally ventilated building sample
compared to the HVAC sample.
Figure 3.12 indicates a statistically significant relationship between the mean level of
thermal insulation worn inside a building and its mean indoor temperature. The
scattergrams in Figure 3.12 suggest that an exponential decay model might fit better than
the straight line printed in the graphs. However, due to the particular weighting
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 95
Central HVAC and Mixed Mode Buildings
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
10 15 20 25 30 35mean indoor operative temperature (oC)
cha
ir +
clo
thin
g (
clo
) insul = 1.73 - 0.04 * top
R2 = 0.18, p = 0.0001
Naturally Ventilated buildings
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
10 15 20 25 30 35
mean indoor operative temperature (oC)
clo
thin
g +
cha
ir (
clo
)
insul = 2.08 - 0.05 * top
R2 = 0.66, p = 0.0001
All Buildings
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
10 15 20 25 30 35
mean indoor operative temperature (oC)
clo
thin
g +
ch
air
(cl
o)
insul = 1.87 - 0.04 * top
R2 = 0.51, p = 0.0001
Figure 3.12: Thermal insulation inside buildings (mean ± stdev) as a function of mean indoor operative temperatures
factors (i.e. sample sizes) applying to each point (building) in the graphs, the R2 statistic was
greatest for the simple linear fits shown in Figure 3.12. The correlation can be described as
“moderate” in the case of the “all buildings” graph of Figure 3.12. The lower panels indicate
that a much stronger relationship in the naturally ventilated buildings. This finding is possibly
due to the greater range of temperatures (independent variable) encountered in the naturally
ventilated building sample compared to the central HVAC/mixed mode building sample.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 96
The error bars either side of the plotted points in Figure 3.12 represent ± one standard
deviation around the within-building mean. All three panels of Figure 3.12 indicate a general
tendency for the standard deviation bars to contract in towards the mean, i.e. the variability
of clothing insulation to decrease, as indoor temperature increased. This possibly reflects a
diminution of degrees of freedom to adjust clothing as the number of individual garments
being worn reduced towards the socially acceptable minimum dress standards.
Central HVAC and Mixed Mode Buildings
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
16 18 20 22 24 26 28 30neutral indoor operative temperature (oC)
clo
thin
g +
ch
air
(cl
o) insul = 1.98 - 0.05 * neut_top
R2 = 0.33, p = 0.0001
Naturally Ventilated Buildings
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
16 18 20 22 24 26 28 30neutral indoor operative temperature (oC)
clo
thin
g +
ch
air
(cl
o)
insul = 1.66 - 0.04 * neut_topR2 = 0.10, p = 0.0607
All Buildings
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
16 18 20 22 24 26 28 30neutral indoor operative temperature (oC)
clo
thin
g +
ch
air
(cl
o)
insul = 1.66 - 0.04 * neut_topR2 = 0.13, p = 0.0002
Figure 3.13: Thermal insulation inside buildings (mean ± stdev) as a function of neutral operative temperature.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 97
Figure 3.13 plots mean thermal insulation inside each building in relation to neutral operative
temperature. While the linear regression model is significant for the “all buildings” panel, the
lower panels of Figure 3.13 suggest that this is primarily attributable to the central HVAC
and mixed-mode buildings.
3.1.6.2. Metabolic rate adjustments indoors
Figure 3.14 depicts mean metabolic heat production estimates within each building in
relation to the mean indoor temperatures prevailing within the building. The error bars
represent ± one standard deviation. Apart from Brown’s eight industrial buildings which had
mean metabolic rates in the 2~3 met unit range, seven of which appear as outliers, the
remaining buildings in the RP-884 database had mean metabolic rates tightly clustered in
the 1.1~1.4 met unit range. Figure 3.14 indicates no discernible relationship between mean
metabolic rates, their within-building standard deviations, or mean temperatures within
buildings.
Figure 3.15 repeats the analysis of mean metabolic rates, this time in relation to the
neutrality observed inside each of the RP-884 database buildings. It seems reasonably
clear that there was no systematic relationship between metabolic rates and the
temperatures which building occupants described as “neutral.”
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 98
Naturally Ventilated Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
10 15 20 25 30 35mean indoor operative temperature (
oC)
me
an
me
tab
olic
ra
te (
me
t) met = 1.09 + 0.003 * topR2 = 0.05, p = 0.1656
Central HVAC and Mixed Mode Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
10 15 20 25 30 35mean indoor operative temperature (
oC)
me
an
me
tab
olic
ra
te (
me
t) met = 1.53 - 0.01 * top
R2 = 0.01, p = 0.4419
All Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
10 15 20 25 30 35mean indoor operative temperature (oC)
me
an
me
tab
olic
ra
te (
me
t)
met = 1.53 - 0.01 * top
R2 = 0.01, p = 0.4419
Figure 3.14: Mean metabolic rates (mean ± stdev) within buildings plotted in relation to mean operative temperature indoors.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 99
Naturally Ventilated Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
16 18 20 22 24 26 28 30neutral indoor operative temperature (
oC)
me
an
me
tab
olic
ra
te (
me
t) met = 1.29 - 0.005 * neut_topR2 = 0.03, p = 0.3454
Central HVAC and Mixed Mode Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
16 18 20 22 24 26 28 30neutral indoor operative temperature (
oC)
me
an
me
tab
olic
ra
te (
me
t) met = 1.32 - 0.01 * neut_top
R2 = 0.003, p = 0.6707
All Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
16 18 20 22 24 26 28 30
neutral indoor operative temperature (oC)
me
an
me
tab
olic
ra
te (
me
t)met = 1.32 - 0.01 * neut_top
R2 = 0.02, p = 0.2134
Figure 3.15: Mean metabolic rates (mean ± stdev) within buildings plotted in relation to neutral operative temperature
3.1.6.3. Air speed adjustments indoors
Table 3.13 presents the means and standard deviations of the within-building mean air
speeds. In all three types of building - HVAC, NV, and mixed-mode, there was a decrement
in mean indoor speeds from summer to winter. The seasonal difference reached statistical
significance in the case of HVAC and NV buildings (T=3.98, df=100, p<0.001 for HVAC;
T=3.62, df=41, p<0.001 for NV). The mean HVAC building air speeds in both summer and
winter samples fell below the draft limit of 0.2 m s-1 specified in ASHRAE Standard 55-92 for
situations in which the occupant has no control. The summer sample in Table 3.13 indicates
mean air speeds within the naturally ventilated buildings were twice as fast as in the HVAC
sample -- this difference was statistically significant (T=7.8, df=103, p<0.001).
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 100
Table 3.13: Statistics for mean indoor air speeds (m s-1).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings in summer sample*
74
5 missing values
31
2 missing values
1
1 missing value
mean VELAV (±stdev) in the summer sample
0.11 (± 0.037)
0.23 (± 0.120)
0.16 (± 0)
number of buildings in winter sample*
28
4 missing values
12
no missing values
2
no missing values
mean VELAV (±stdev) in the winter sample
0.08 (± 0.024)
0.10 (± 0.047)
0.12 (± 0.033)
Figure 3.16 plots the within-building means and standard deviations for indoor air speeds in
relation to mean temperatures indoors. The top panel indicates that almost half of the
between-building variance in the mean indoor air speeds could be accounted for by
variations in mean temperatures. The relationship was best approximated by a model that
expressed indoor air speed as an exponential function of indoor temperature. The lower
panels in Figure 3.16 indicate that this statistically significant relationship extended to both
central HVAC and naturally ventilated buildings, although the relationship was a simple linear
one in the case of HVAC buildings. This was probably because the range of mean
temperatures and mean air speeds observed in the HVAC building sample was relatively
restricted. But even across this restricted range of temperatures, the variability of indoor air
speeds, as indicated by the error bars (mean ±stdev) in Figure 3.16, tended to increase as
mean indoor temperatures increased. A stronger dependence of mean air speeds on mean
temperatures was observed in the sample of naturally ventilated buildings (lower right-hand
panel of Figure 3.16). The relationship for these buildings was clearly exponential and the
model was capable of explaining 53% of the between-building variance in mean air speeds.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 101
Central HVAC and Mixed Mode Buildings
0.0
0.2
0.4
0.6
0.8
1.0
10 15 20 25 30 35mean indoor operative tempetature (
oC)
me
an
ind
oo
r ve
loci
ty (
m/s
) vel = -0.56 + 0.03 * top
R2 = 0.34, p = 0.0001
Naturally Ventilated Buildings
vel = 0.0077e 0.1174 * top
R2 = 0.5312, p < 0.05
0.0
0.2
0.4
0.6
0.8
1.0
10 15 20 25 30 35mean indoor operative temperature (
oC)
me
an
ind
oo
r ve
loci
ty (
m/s
)
All Buildings
vel = 0.0048e0.1314 * top
R2 = 0.4664, p < 0.05
0.0
0.2
0.4
0.6
0.8
1.0
10 15 20 25 30 35mean indoor operative temperature (
oC)
me
an
ind
oo
r ve
loci
ty (
m/s
)
Figure 3.16: Indoor air speeds (building mean ± stdev) plotted in relation to mean operative indoor temperature.
Figure 3.17 examines the relationship between mean indoor air speed and the temperature
judged as “neutral” by each building’s occupants. As with the regressions on mean
temperature, the air speed observations were best approximated by a simple linear model
in the HVAC building sample. But the relationship across the extended range of dependent
and independent variables in naturally ventilated buildings was best approximated with an
exponential model, accounting for 35% of the between-building variance in mean speeds
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 102
Central HVAC and Mixed Mode Buildings
0.0
0.2
0.4
0.6
0.8
1.0
16 18 20 22 24 26 28 30neutral indoor operative temperature (
oC)
me
an
ind
oo
r ve
loci
ty (
m/s
) vel = -0.16 + 0.01 * neut_top
R2 = 0.08, p = 0.0288
Naturally Ventilated Buildings
vel = 0.0068e 0.1358 * neut_top
R2 = 0.354, p < 0.05
0.0
0.2
0.4
0.6
0.8
1.0
16 18 20 22 24 26 28 30
neutral indoor operative temperature (oC)
me
an
ind
oo
r ve
loci
ty (
m/s
)
All Buildings
vel = 0.005e0.137 * neut_top
R2 = 0.2407, p < 0.05
0.0
0.2
0.4
0.6
0.8
1.0
16 18 20 22 24 26 28 30
neutral indoor operative temperature (oC)
me
an
ind
oo
r ve
loci
ty (
m/s
)
Figure 3.17: Indoor air speeds (building mean ± stdev) plotted in relation to neutral operative temperature.
3.2. Interactions with outdoor weather and climate
Much of what has been published to date on the subject of adaptive models of comfort has
emphasised the role of external climatic environment in forcing behavioral adjustments,
physiological acclimatization and thermal expectations. This section presents comfort data
from the RP-884 database in relation to the weather and climatic conditions prevailing
outside the study buildings.
3.2.1. Thermal neutrality and outdoor climate
Outdoor climate can be represented in these meta-analyses at two levels of detail:
a) simple seasonal comparisons (summer v winter), and
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 103
b) mean outdoor average ET* (dayav_et) during the period of each building’s survey
3.2.1.1. Seasonal comparisons
The raw data files obtained from original field researchers were classified by the RP-884
team into summer or winter, depending on month and location. Tropical locations were all
regarded as summer regardless of month.
Table 3.14: Seasonal comparisons of thermal neutralities* defined in terms of the four major indoor thermal indices (TOP, ET, PMV and SET)
centrally heated/air-conditioned buildings
naturally ventilated buildings
t-test of the difference between the summer and winter samples for neut_top
t = 4.50 df = 59
p < 0.001
t = 2.09 df = 32
p < 0.05 t-test of the difference between the summer and winter samples for neut_et
t = 3.76 df = 59
p < 0.001
t = 1.47 df = 32 p > 0.1
t-test of the difference between the summer and winter samples for neut_pmv
t = 4.14 df = 28
p < 0.001
t = 1.08 df = 25 p > 0.2
t-test of the difference between the summer and winter samples for neut_set
t = 0.8 df = 30 p > 0.2
t = 3.47 df = 25
p < 0.002 * only results from buildings with statistically significant regression models in
Appendix A were used in this table
Thermal neutralities defined in terms of the simpler indices of operative temperature and
new effective temperature were significantly differentiated between seasons in centrally
heated or air-conditioned buildings, with the average neutrality in summer being about one
and a half degrees (K) warmer than in winter. However, their seasonal differences for
neutralities on the more sophisticated thermal indices of PMV and SET reached statistical
significance in Table 3.14 only for the PMV index.
The smaller sample size of naturally ventilated buildings in the RP-884 meant that the
seasonal comparisons in neutralities were less clear cut than they were for HVAC buildings
-- e.g. only two of the naturally ventilated buildings in winter achieved significant SET
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 104
regression models. As a result, only neutral operative temperatures and neutral Standard
Effective Temperatures were significantly differentiated between the summer and winter
seasons in Table 3.14. While the seasonal difference in neutral operative temperature was
over two degrees (K) and in the direction one might expect from the adaptive model’s
perspective, it should be pointed out that SET index neutrality difference between seasons
had a counterintuitive sign -- the winter neutrality was warmer than the summer one.
Not listed in Table 3.14 due to small sample sizes, but worth mentioning is the seasonal
difference in neutrality for buildings classified as “mixed mode”. Only one such building
managed a significant regression model with operative temperature in summer, but its
neutrality in that season was 23.9°C compared to the average winter neutrality of 20.7°C
recorded in such buildings.
3.2.1.2. Dependence of observed neutrality on outdoor climate
Linear regression models were constructed for the relationship between indoor thermal
neutrality and mean outdoor warmth. The former was assessed in terms of the operative
temperature index (neut_top) while outdoor warmth was parameterized in terms of mean
daily effective temperature (dayavet). Figure 3.18 shows the resulting regression models,
and the “all buildings” panel indicates a reasonably strong correlation for this relationship,
with r = +0.65. The regression coefficient in that model suggests that operative temperature
neutrality indoors changes by one degree (K) for about six degrees change in mean daily
outdoor effective temperature.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 105
all buildings from the RP-884 database
15
17
19
21
23
25
27
29
-5 0 5 10 15 20 25 30 35mean outdoor effective temperature (oC)
neut
ral i
ndoo
r ope
rativ
e te
mpe
ratu
re (
o C)
neutrality = 20.9 + 0.16 (outdoor ET*)
R2 = 0.42, p = 0.0001
Central HVAC and Mixed Mode buildings, from the RP-884 database
15
17
19
21
23
25
27
29
-5 0 5 10 15 20 25 30 35
mean outdoor effective temperature (oC)
ne
utr
al i
nd
oo
r o
pe
rati
ve
tem
pe
ratu
re (o C
)
neut_top = 21.5 + 0.11 * dayavet
R2 = 0.53, p = 0.0001
naturally ventilated Buildings from the RP-884 database
15
17
19
21
23
25
27
29
-5 0 5 10 15 20 25 30 35
mean outdoor effective temperature (oC)
ne
utr
al i
nd
oo
r o
pe
rati
ve
tem
pe
ratu
re (o C
)
neut_top = 18.9 + 0.255 * dayavet
R2 = 0.42, p = 0.0001
Figure 3.18: Dependence of indoor neutrality on outdoor climate
The slope of the model for naturally ventilated buildings in Figure 3.18 indicates that indoor
thermal neutrality increased by approximately one degree (K) for every four degree (K)
increase in mean daily outdoor effective temperature (dayavet). The gradient for centralized
HVAC buildings was less than half the naturally ventilated buildings’ result. Using the T-test
method for comparing two straight lines using separate regression fits, as described in
Kleinbaum et al. (1988), we obtained a T statistic of 3.25 (d.f.=101), which was statistically
significant (p < 0.01).
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 106
3.2.1.3. Analysis of predicted neutralities with respect to mean outdoor temperature
The PMV model was used to predict thermal neutralities for each of the buildings in the RP-
884 database simply by using building mean values for each of the heat balance model’s
input parameters, and then iterating the operative temperature input until PMV=0. The
predicted neutralities, codenamed predneut, have been plotted in Figure 3.19 in relation to
mean daily average outdoor effective temperatures. With the exception of Brown’s seven
HVAC light industrial buildings, predicted neutralities demonstrate a moderate linear
dependence on outdoor climate. The anomalous industrial buildings probably result from the
significantly higher metabolic rates of their occupants. Metabolic rate is one of the six input
parameters to the heat balance model which was used to predict these neutralities. Figure
3.20 shows the regression model for HVAC buildings with these outliers excluded from the
analysis. While the regression coefficient changed little after these exclusions, the amount of
variance in predneut accounted for by the model increased to 25%
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 107
All Buildings
10
15
20
25
30
-30 -20 -10 0 10 20 30 40
mean outdoor effective temperature (oC)
pred
icte
d ne
utra
lity
(oC
)
predneut = 21.6 + 0.08 * dayavet
R 2 = 0.14, p = 0.0001
Central HVAC and Mixed Mode Buildings
10
15
20
25
30
-30 -20 -10 0 10 20 30 40
mean outdoor effective temperature (oC)
pred
icte
d ne
utra
lity
(oC
)
predneut = 21.5 + 0.07 * dayavet
R2 = 0.10, p < 0.001
Naturally Ventilated Buildings
10
15
20
25
30
-30 -20 -10 0 10 20 30 40
mean outdoor effective temperature (oC)
pred
icte
d ne
utra
lity
(o C)
predneut = 21.6 + 0.12 * dayavet
R 2 = 0.30, p < 0.001
Figure 3.19: Dependence of PMV-based neutrality predictions on outdoor climate
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 108
Central HVAC and Mixed Mode Buildings
10
15
20
25
30
-30 -20 -10 0 10 20 30 40
mean outdoor effective temperature (oC)
pred
icte
d ne
utra
lity
(oC
)
predneut = 22.6 + 0.04 * dayavetR
2 = 0.25, p <0.001
Figure 3.20: Dependence of PMV-based neutrality predictions on outdoor climate for HVAC buildings with sedentary occupants
The statistical significance of regression models for PMV-predicted neutrality, as plotted in
Figures 3.19 and 3.20, probably reflects the dependence of some of the basic heat-balance
variables such as clothing insulation and indoor air speed on outdoor climate. These
mediating variables will be subjected to further detailed analysis in relation to outdoor
climate in a subsequent section dealing with behavioral responses (Section 3.2.4).
The predicted neutralities in naturally ventilated buildings (Figure 3.19) were almost twice as
sensitive to outdoor temperature than was the case for HVAC buildings (Figure 3.19), and
this was confirmed with Kleinbaum’s (1988) statistical test for the difference between
independent regression coefficients (T=3.64, df=148, p<0.01).
3.2.2. Thermal acceptability and outdoor climate
Section 3.1.3 indicated that thermal satisfaction ratings within buildings bore little
relationship with mean indoor climatic indices (Figure 3.6). This section examines whether
or not thermal acceptability is related to outdoor climate in any way. Figure 3.21 presents
polynomial regression models separately for a) directly assessed thermal acceptability (tsa),
and b) thermal acceptability inferred from thermal sensation votes (prxy_tsa). The y-axis
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 109
variables in both graphs represent the number of occupants within each building expressing
thermal acceptability as a percentage of the whole building sample. Both direct and inferred
versions of building thermal acceptability ratings failed to show any signs of a statistically
significant relationship with outdoor climate in Figure 3.21.
Directly assessed thermal acceptability, all buildings
0
20
40
60
80
100
-30 -20 -10 0 10 20 30 40
mean outdoor effective temperature (oC)
TSA
(% a
ccep
tabl
e)
tsa = 72.78 - 0.30 * dayavet + 0.02 * dayavet2
R2 = 0.06, p = 0.1683
Thermal acceptability inferred from thermal sensation votes, all buildings
0
20
40
60
80
100
-30 -20 -10 0 10 20 30 40mean outdoor effective temperature (oC)
Pro
xy T
SA
(%
acce
ptab
le)
prxy_tsa = 79.66 - 0.25 * dayavet + 0.01 * dayavet 2
R2 = 0.003, p = 0.7682
Figure 3.21: Thermal acceptability and outdoor climate. TSA represents directly assessed thermal acceptability levels within each building and PRXY_TSA represents thermal acceptability inferred from thermal sensation votes.
A building’s thermal acceptability rating, as inferred from thermal sensation votes, is logically
related to the gradient of that building’s thermal sensation regression models with respect to
indoor temperature (Appendix A). The range of acceptable operative temperatures for each
building was defined as rang_top in the RP-884 meta-analysis and presented earlier in
Section 3.1.3.4 simply by solving the mean thermal sensation versus mean indoor operative
temperature regression model (Appendix A) for mean ASHRAE thermal sensation votes of
±0.85. These values were chosen on the basis of Fanger’s PMV/PPD model (Fanger,
1970) which suggests they correspond to 80% acceptability levels (PPD=20%).
The complete lack of any statistical relationship between the range of acceptable indoor
operative temperatures (rang_top) and mean daily outdoor effective temperature (dayavet)
is evident in Figure 3.22.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 110
Central HVAC and Mixed Mode Buildings
0
2
4
6
8
10
12
14
-5 0 5 10 15 20 25 30 35
mean outdoor effective temperature (oC)
rang
_top
(K)
rang_top = 4.07 + 0.02 * dayavet
R2 = 0.01, p = 0.8460
Naturally Ventilated Buildings
0
2
4
6
8
10
12
14
-5 0 5 10 15 20 25 30 35mean outdoor effective temperature (oC)
rang
_top
(K)
rang_top = 8.63 - 0.04 * dayavetR2 = 0.01, p = 0.6752
Figure 3.22: The range of acceptable operative temperatures indoors plotted in relation to the mean outdoor effective temperature.
3.2.3. Thermal preference and outdoor climate
Section 2.8.5 described how preferred temperature was derived for each building with the
MCI questionnaire item by locating the intersection of the “want cooler” probit model with the
“want warmer” model (see Appendix B). The indoor operative temperature corresponding
with the intersection of “cooler” and “warmer” probit models was incorporated into the RP-
884 meta-analysis as preftemp. Conceptually, preftemp is directly analogous to the
neutrality (neut_top), only it was derived from thermal preference votes instead of thermal
sensations. Regression models of the dependence of preftemp on mean outdoor effective
temperature (dayavet) are presented in Figure 3.23.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 111
All Buildings
18
20
22
24
26
28
30
-25 -20 -15 -10 -5 0 5 10 15 20 25 30 35
mean outdoor effective temperature (oC)
pref
erre
d te
mpe
ratu
re (
oC
)
preftemp = 22.49 - 0.01 * dayavet + 0.003 * dayavet2
R2 = 0.13, p = 0.0004
Central HVAC and Mixed Mode Buildings
18
20
22
24
26
28
30
-25 -15 -5 5 15 25 35mean outdoor effective temperature (
oC)
pref
erre
d te
mpe
ratu
re (
oC
) preftemp = 22.82 - 0.001*dayavet + 0.001 * dayavet2
R2 = 0.05, p = 0.3871
Naturally Ventilated Buildings
preftemp = 19.3 + 0.22 dayavet
R2 = 0.43 p<0.00001
18
20
22
24
26
28
30
-25 -15 -5 5 15 25 35mean outdoor effective temperature (
oC)
pref
erre
d te
mpe
ratu
re (
oC
)
Figure 3.23: Dependence of indoor preferred temperatures on outdoor climate
The regression models show a relatively weak relationship between indoor temperature
preferences and outdoor climate in the “all buildings” panel of Figure 3.23. A slightly
parabolic trend is discernible in the plot, but there is insufficient data from climates with sub-
zero mean daily outdoor effective temperatures to be confident about this trend. The
temperature preference data become clearer when they are plotted separately for the HVAC
and NV building sub-samples in Figure 3.23. Basically there is no discernible relationship in
the case of HVAC and mixed-mode buildings, with the second order polynomial model
failing to reach statistical significance at the 0.05 level. However, the naturally ventilated
sub-sample of preferred temperatures, albeit small (n=30), demonstrates a clear
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 112
relationship with outdoor climate, with the linear regression model explaining 43% of the
variance (r= +0.66). The NV subsample’s scatter plot in Figure 3.23 suggest a slightly
curvilinear relationship, and although the addition of a second-order polynomial term in the
regression model lifted the R2 statistic to 46%, neither the first-order nor second-order
regression coefficients reached statistical significance (T=0.55, p=0.54; T=1.12, p=0.25 for
X1 and X2 terms respectively), so the simple linear regression model has been retained for
Figure 3.23.
Since preferred temperatures represent an alternative definition to thermal neutrality for
optimal indoor temperature, it becomes interesting to compare them. Traditionally, thermal
comfort researchers have regarded thermal neutrality and preference as synonymous, but
some published evidence suggests that there may in fact be a semantic discrepancy in the
way the two scales are actually interpreted by building occupants (McIntyre, 1978; de Dear
et al, 1991c). This semantic artefact hypothesis predicts that thermal neutralities will shift to
warmer temperatures than actually preferred when the climatic context is warm, while the
offset will be cooler-than-preferred in cold climatic contexts. Figure 3.24 tests this
hypothesis by plotting the discrepancy between neutrality and preference (discrep) in
relation to mean outdoor effective temperature. The main panel (“all buildings”) depicts a
statistically significant weighted regression model with the slope as predicted by the
semantic artefact hypothesis. However, the linear fit is very poor, with the model’s gradient
indicating that thermal neutrality drifts apart from preference at the rate of only one degree
(K) for every 25 K shift in mean outdoor effective temperature. The model fitted to the small
sample of naturally ventilated buildings failed to achieve any statistical significance, but the
same was not true for the HVAC and mixed-mode sample of buildings. The lower left panel
of Figure 3.24 indicates the linear model accounted for about 38% of the variance in discrep
and that thermal neutrality diverged from preference at the rate of about one degree (K) for
every 14 K change in outdoor temperature.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 113
All Buildings
-4
-3
-2
-1
0
1
2
3
-5 0 5 10 15 20 25 30 35
mean outdoor effective temperature (oC)
dis
cre
p (
oC
)
discrep = -0.42 + 0.04 * dayavet
R 2 = 0.06, p = 0.0195
Central HVAC and Mixed Mode Buildings
-4
-3
-2
-1
0
1
2
3
-5 0 5 10 15 20 25 30 35
mean outdoor effective temperature (oC)
dis
cre
p (
oC
)
discrep =-0.95 + 0.07 * dayavet
R 2 = 0.38, p = 0.0001
Naturally Ventilated Buildings
-4
-3
-2
-1
0
1
2
3
-5 0 5 10 15 20 25 30 35mean daily outdoor effective temperature (
oC)
dis
cre
p (
oC
)
discrep = 0.23 + 0.01 * dayavet
R2 = 0.001, p = 0.8501
Figure 3.24: The discrepancy (discrep) between thermal neutrality (neut_top) and preference (preftemp) plotted against mean outdoor climate (dayavet).
3.2.4. Behavioural responses to outdoor climate
Since all of the input variables to the heat balance model of thermal comfort are available
within the RP-884 database, it is possible to explore their variations and relationships with
respect to outdoor climate. This subsection focuses on the main adaptive adjustments
involved in the human body’s heat balance with indoor climate -- in particular, clothing
insulation, metabolic rate and indoor air speed.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 114
3.2.4.1. Indoor clothing and outdoor climate
The amount of clothing insulation worn indoors was shown in Section 3.1.6.1. to be related to
indoor climatic indices such as mean indoor operative temperature. It seems reasonable to
expect that clothing decisions and behavior are also influenced by outdoor weather and
climatic differences. To examine this possibility Figure 3.25 plots mean thermal insulation
values for the occupants of each building (insul), comprised of both clothing and chair
components, against mean outdoor effective temperatures prevailing at the time of the
building samples survey. For the HVAC and naturally ventilated building samples combined,
40% of the variance in clo values was explained by variations in the outdoor climatic index.
An exponential decay curve fitted the data significantly better than a straight line model,
probably reflecting the effects of a minimum socially acceptable level of thermal insulation at
about 0.4 clo units (after subtracting 0.15 clo units for chair effects).
The error bars (standard deviations) around each building point plotted in Figure 3.25
suggest a trend towards increasing homogeneity in thermal insulation for those buildings
located in warmer climates. This presumably also reflects the fact that clothing decisions
and behavior have fewer degrees of freedom as the level of clothing approaches the
minimum socially acceptable threshold.
When the clothing database was disaggregated by building type (HVAC v NV), thermal
insulation was also found to decay exponentially with outdoor temperature in the HVAC
buildings where the regression model was found to account for about 64% of the variance in
insul. However, in the case of naturally ventilated buildings, a straight line regression model
produced the best fit to the data, with only 44% of variance being explained (r= -0.66). The
rate of insulation change with respect to outdoor temperature within the naturally ventilated
buildings was almost one tenth of a clo unit for every three degrees (K) of outdoor effective
temperature change, and this gradient appears in Figure 3.25 to be significantly steeper
than in the HVAC and mixed-mode buildings.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 115
Central HVAC and Mixed Mode Buildings
insul = 0.9343e-0.0127 * dayavet
R 2 = 0.64, p < 0.05
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
chai
r +
clot
hing
(cl
o)
Naturally Ventilated Buildings
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
clot
hing
+ch
air
(clo
)
insul = 1.44 - 0.03 * dayavet
R 2 = 0.44, p = 0.0001
All Buildings
insul = 0.9346e-0.0133 * dayavet
R2 = 0.40, p < 0.05
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
-25 -20 -15 -10 -5 0 5 10 15 20 25 30 35
mean outdoor effective temperature (oC)
clot
hing
+ch
air
(clo
)
Figure 3.25: Building occupants’ thermal insulation (clothing plus chair) as a function of outdoor temperature
3.2.4.2. Metabolic rate indoors related to outdoor climate
Figure 3.26 indicates a complete absence of any systematic relationship between mean
metabolic rates registered within buildings and the mean outdoor temperature prevailing at
the time of the survey. This generalization applies to both HVAC and naturally ventilated
buildings.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 116
Central HVAC and Mixed Mode Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
mea
n m
etab
olic
rat
e (m
et)
met = 1.21 - 0.0005 * dayavet
R2 = 0.001, p = 0.7527
Naturally Ventilated Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
mea
n m
etab
olic
rat
e (m
et)
met = 1.13 + 0.002 * dayavet
R2 = 0.03, p = 0.2851
All Buildings
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
-25 -20 -15 -10 -5 0 5 10 15 20 25 30 35
mean daily outdoor effective temperature (oC)
mea
n m
etab
olic
rat
e (m
et)
met = 1.18 - 0.000004 * dayavet
R 2 = 0.00, p = 0.9977
Figure 3.26: Metabolic rates of building occupants plotted in relation to mean outdoor climate
3.2.4.3. Indoor air speeds in relation to outdoor climate
Occupants of buildings, both with and without centralized HVAC, tend to increase indoor air
movement when or where temperatures increase. In the case of naturally ventilated
buildings in humid climates, this is typically achieved by opening windows and turning on
fans. In very hot and dry climates, windows are often kept shut, leaving just indoor fans to
accelerate air movement within the occupied zone. The same is often the case in buildings
with centralized HVAC services -- because windows are typically sealed, occupants resort
to using localised fans to supplement the typically low levels of air movement generated
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 117
within the occupied zone by conventional HVAC diffusers. All of this applies to situations of
elevated indoor temperature. The present section explores the relationship between indoor
air speeds and outdoor climate. Obviously indoor temperatures correlate with those
outdoors for naturally ventilated buildings, but this should not be the case in HVAC buildings
if the current (static) thermal comfort standards (ISO and ASHRAE) are being strictly
applied.
Central HVAC and Mixed Mode Buildings
velav = 0.08e0.0135 * dayavet
R2 = 0.19, p<0.05
0.0
0.2
0.4
0.6
0.8
1.0
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
mea
n in
door
vel
ocity
(m
/s)
Naturally Ventilated Buildings
velav = 0.03 e0.0758 * dayavet
R2 = 0.64, p < 0.05
0.0
0.2
0.4
0.6
0.8
1.0
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
mea
n in
door
vel
ocity
(m
/s)
All Buildings
velav = 0.07 e0.0258 * dayavet
R 2 = 0.25, p < 0.05
0.0
0.2
0.4
0.6
0.8
1.0
-25 -20 -15 -10 -5 0 5 10 15 20 25 30 35
mean outdoor effective temperature (oC)
mea
n in
door
vel
ocity
(m
/s)
Figure 3.27: Relationship between indoor air speeds and outdoor climate
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 118
Figure 3.27 plots mean building air speeds against the mean outdoor effective temperature
at the time of the building’s survey. The error bars around each data point represents ± one
standard deviation. The main panel of Figure 3.27 indicates a moderate correlation
between mean indoor air speeds and mean outdoor temperatures (r= +0.50). An
exponential function rather than straight line achieved the best fit to the data, reflecting the
effects of a minimum mean air speed of about 0.05 m s-1. The graph demonstrates clearly
that not only the mean speed within a building increases as outdoor temperature increases,
but also the variability in speeds around the mean increases.
These generalizations extend to the separate HVAC and NV samples’ analyses in Figure
3.27. The model fitted to the HVAC and mixed-mode sample indicates a modest increase
in air speeds from an average of below 0.10 m s-1 in cold climates to about 0.2 m s-1 in the
hot climates. Whether this increase is due to increased air speeds from HVAC diffusers or
local air movement generated by desk, ceiling or floor fans is unsure, but the relationship
accounted for 23% of variance in mean buildings air speeds (r=0.48). The strongest
correlation in Figure 3.27 was found in the naturally ventilated buildings where an exponential
regression model accounted for almost 64% of the variance in the dependent variable (r=
+0.80). Mean air speeds in cold-climate naturally ventilated buildings were similar to those
in HVAC buildings, below 0.10 m s-1, but increased to values in excess of 0.4 m s-1 in the
warmer climates represented in the RP-884 database.
3.3. Influence of building characteristics on thermal comfort
Comparisons between thermal comfort experiences of HVAC buildings and naturally
ventilated buildings have been made throughout the preceding sections of this chapter and
several significant differences have been discussed. The present section goes further into
the analyses of these contextual factors (as opposed to indoor or outdoor climatic features),
including the index of perceived control which we introduced in Section 2.4.
3.3.1. HVAC versus natural ventilation
The regression gradients depicted for each building in Appendix A suggest that thermal
sensations were about twice as sensitive to changes in indoor operative temperature in
centrally heated and air-conditioned buildings than in naturally ventilated buildings.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 119
Unfortunately the sample size of mixed mode buildings was too small to draw any conclusive
comparisons, but the mean gradient fell, as might be expected of such buildings, about
midway between that for HVAC and naturally ventilated buildings. This heightened
sensitivity in HVAC buildings extended to the ET index as well, but did not persist when the
fully developed heat-balance indices such as PMV and SET were subjected to the
regression analyses, suggesting that factors such as clothing insulation and air speed were
responsible for any differences observed when using the simpler indoor climatic indices.
3.3.1.1. Thermal sensation and sensitivity in HVAC versus naturally ventilated buildings
Comparisons between thermal neutralities in centrally heated/air-conditioned building
sample with those in the naturally ventilated buildings have been summarised in Table 3.15.
Table 3.15: Comparisons of thermal neutrality observed in the HVAC and naturally ventilated building samples
Neutrality defined in terms of four indoor climatic indices
summer sample
winter sample
t-test of the difference between HVAC and NV buildings for neut_top
t = 1.16 df = 72 p > 0.1
t = 0.14 df = 19 p > 0.5
t-test of the difference between HVAC and NV buildings for neut_et
t = 1.12 df = 70 p > 0.2
t = 1.00 df = 21 p > 0.2
t-test of the difference between HVAC and NV buildings for neut_pmv
t = 1.48 df = 47 p > 0.1
not valid due to low
sample size t-test of the difference between HVAC and NV buildings for neut_set
t = 0.61 df = 47 p > 0.5
not valid due to low
sample size * only results from buildings with statistically significant regression models used in
these comparisons
None of the HVAC - NV comparisons in Table 3.15 reached statistical significance. That is,
in summer or in winter, there was no significant difference in neutrality between centrally
conditioned and naturally ventilated buildings, regardless of which thermal index was used to
define it.
As noted earlier in Table 3.1, Section 3.1.1.1, thermal sensitivity (i.e. the rate of change in
sensation votes, ASH, with respect to indoor temperature) was greater in the centrally
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 120
heated/cooled buildings than in naturally ventilated buildings within the database. This was
particularly so when indoor warmth was defined simply in terms of operative or effective
temperature indices. But again, when the fully-developed heat balance indices of indoor
warmth, namely PMV and SET, were substituted into the analysis, these differences
became less significant, suggesting that heightened thermal sensitivity in HVAC buildings
was at least partly the result of other heat balance factors such as clothing and air speed
remaining relatively static. Conversely, the relative thermal insensitivity of occupants of
naturally ventilated buildings appears to be largely the result of the ability to manipulate
physical variables affecting their body’s heat balance. Table 3.16 below summarises the
comparisons between classes of buildings in the database.
Table 3.16: Comparison of thermal sensitivity for centrally controlled buildings (HVAC) and naturally ventilated buildings (NV)
ASH v TOP ASH v ET ASH v PMV ASH v SET model mean gradient for HVAC buildings 0.51
0.50 0.74 0.21
model mean gradient for NV buildings 0.27 0.28 0.62 0.18
t-test of the difference between HVAC and NV model gradient means
t = 5.37 df = 97
p < 0.001
t = 4.45 df = 96
p < 0.001
t = 1.56 df = 56 p > 0.1
t = 1.00 df = 57 p > 0.2
* only results from buildings with statistically significant regression models used
The question left unanswered in this table is “why are occupants of naturally ventilated
buildings inclined to behaviorally regulate their heat balance to a greater extent than their
counterparts within HVAC buildings?” Is it the result of greater adaptive opportunities in
naturally ventilated buildings, particularly with respect to air speed, or is it the result of a
reluctance to thermoregulate with clothing adjustments on the part of HVAC building
occupants? Questions of adaptive opportunity and perceived control will be examined later
in Section 3.3.2.
3.3.1.2. Thermal acceptability in HVAC versus naturally ventilated buildings
Earlier sections have demonstrated little systematic relationship, if any, between directly
assessed thermal acceptability ratings and physical measurements of indoor climate. The
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 121
percentage of building occupants voting “acceptable” was, on average, 81.6% in centrally
air-conditioned buildings with a standard deviation of acceptability ratings at 10.1%. The
mean rating is just at the minimum acceptability threshold suggested in the thermal comfort
standards such as ASHRAE 55 and ISO 7730. The corresponding mean for naturally
ventilated buildings was 66.8%, and the between-buildings standard deviation of 20.1% was
twice that of HVAC sample. The difference of less than 15% acceptability, whilst statistically
significant (T=3.74; df=59, p<0.001), suggests that centralized air-conditioning enhances
perceived quality of internal environments only moderately. This interpretation, however,
ignores questions about where the two samples were drawn from -- were the naturally
ventilated buildings selected for the RP-884 database drawn from mild climate zones? Did
the HVAC building sample cover a much broader spectrum of climates, including some
which may have rendered passive architectural alternatives infeasible?
Since directly assessed thermal acceptability ratings were not universally available
throughout the database, attention turns to the indirect assessments derived from thermal
sensation votes. An earlier section of the current chapter gave the definition of “range of
acceptable temperatures” as those coinciding with mean thermal sensations of ±0.85 on the
linear regression models of Appendix A. This temperature range for each building,
codenamed rang_top in the meta-analysis, is inversely related to thermal sensitivity, as
noted in the preceding section. Therefore it is not surprising to find that the mean rang_top
in naturally ventilated buildings was about 70% wider than in centrally air-conditioned
buildings (see Table 3.9). The significance of the difference in acceptable temperature
ranges between HVAC and NV buildings was retained when the thermal index switched to
SET (i.e. the RANG_SET variable T=2.49, df=55, p<0.02). This implies that the extended
range of acceptability within naturally ventilated buildings could not be accounted for purely in
terms of physical heat-balance adjustments (clothing and air speed), and that other types of
adaptive response such as acclimatisation and shifting expectations may indeed influence
thermal acceptability.
3.3.1.3. Thermal preferences in HVAC versus naturally ventilated buildings.
Appendix B established the preferred operative temperatures for each building in which
some variant of the so-called “McIntyre scale” (MCI) was presented on the questionnaire.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 122
The present section examines the dependence of preferred temperatures on indoor and
outdoor warmth, separately, for HVAC and naturally ventilated buildings during winter and
summer seasons. Figure 3.28 below presents the relationship between thermal preferences
and indoor temperature.
Central HVAC and Mixed Mode Buildings in Summer
18
20
22
24
26
28
30
18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
pre
ferr
ed
te
mp
era
ture
(o
C)
preftemp = 30.38 - 0.31 * topR2 = 0.03, p = 0.1618
Naturally Ventilated Buildings in Summer
18
20
22
24
26
28
30
18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
pre
ferr
ed
te
mp
era
ture
(oC
)preftemp = 13.28 + 0.39 * top
R2 = 0.34, p = 0.0029
Central HVAC and Mixed Mode Buildings in Winter
18
20
22
24
26
28
30
18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
pre
ferr
ed
te
mp
era
ture
(o C)
preftemp = 16.22 + 0.29 * topR2 = 0.02, p = 0.5165
Naturally Ventilated Buildings in Winter
18
20
22
24
26
28
30
18 20 22 24 26 28 30 32
mean indoor operative temperature (oC)
pre
ferr
ed
te
mp
era
ture
(o C) preftemp = 13.52 + 0.47 * top
R2 = 0.50, p = 0.1142
Figure 3.28: Dependence of thermal preferences on mean indoor warmth for HVAC and naturally ventilated buildings
The relatively tight temperature control within HVAC (and mixed-mode buildings) is reflected
as a restricted range on abscissa for Figure 3.28 and the statistically insignificant
regression models fitted across this narrow band of the independent variable suggest
thermal preferences were unrelated to mean temperatures inside HVAC buildings.
However, the naturally ventilated buildings in Figure 3.28 present a significantly different
picture, particularly those buildings sampled during summer, in which we found that the
preferred temperature increased by one degree (K) for every 2.5 K increase in mean indoor
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 123
temperature. The same trend was apparent in the winter sample as well, but due to the
limited number of NV buildings sampled in that season, the regression model failed to reach
statistical significance in the lower-right panel of Figure 3.28.
Central HVAC and Mixed Mode Buildings in Summer
18
20
22
24
26
28
30
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
pre
ferr
ed
te
mp
era
ture
(oC
)
preftemp = 23.19 - 0.01 * dayavet
R2 = 0.0004, p = 0.8738
Naturally Ventilated Buildings in Summer
18
20
22
24
26
28
30
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
pre
ferr
ed
te
mp
era
ture
(o C) preftemp = 17.29 + 0.29 * dayavet
R2 =0.41, p = 0.0008
Central HVAC and Mixed Mode Buildingsin Winter
18
20
22
24
26
28
30
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
pre
ferr
ed
te
mp
era
ture
(oC
)
preftemp = 23.09 - 0.05 * dayavetR2 = 0.11, p = 0.1139
Naturally Ventilated Buildings in Winter
18
20
22
24
26
28
30
-25 -15 -5 5 15 25 35
mean outdoor effective temperature (oC)
pre
ferr
ed
te
mp
era
ture
(o C) preftemp = 19.78 + 0.26 * dayavet
R2 = 0.47, p = 0.1352
Figure 3.29: Dependence of thermal preferences on mean outdoor warmth for HVAC and naturally ventilated buildings
Figure 3.29 examines temperature preferences within HVAC and naturally ventilated
buildings in relation to mean outdoor temperatures for summer and winter seasons. As was
the case for the indoor analyses just presented in Figure 3.28, the insignificant models
suggest that thermal preferences within HVAC buildings apparently have no systematic
relationship with the temperatures prevailing outdoors. This generalization does not extend
to naturally ventilated buildings, especially in the summer season where the RP-884
database contains a reasonable sample size. Figure 3.29 indicates that the operative
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 124
temperature preferred inside such buildings increased by about one degree (K) for every
three degrees (K) increase in mean daily outdoor effective temperature.
3.3.2. Personal environmental control
The literature review of the adaptive thermal comfort hypothesis in Chapter 1 indicated that
concepts of adaptive opportunity and perceived control play an important part in the
processes of thermal perception. Unfortunately only a handful of field research projects
within the RP-884 database explicitly included questionnaire items on these issues, and
those that did may not have used directly comparable versions of questionnaire. The
simple, albeit crude, solution proposed in the RP-884 database was to infer levels of
perceived control for each building in the sample from the following items of information:
• a knowledge of which adaptive opportunities were available within the building (operable
windows, doors, thermostats, fans, blinds etc), and
• how much each of these adaptive opportunities contributed to overall levels of perceived
control. This second step was quantified on the basis of a sub-sample of RP-884
buildings where the relevant questionnaire items were available for detailed analysis (see
Section 2.4).
A synthesis of these details led to an index of perceived control (PCC_AG) for about two
thirds of the buildings within the RP-884 database. This section of the RP-884 final report
presents some basic descriptive statistics for this index and some preliminary analyses of
its relationship with thermal perception (sensation, acceptability and preference).
Table 3.17: Summary of the perceived control index (pcc_ag).
centrally heated/air-conditioned buildings
naturally ventilated buildings
mixed-mode buildings
number of buildings
76 (35 missing values)
30 (15 missing values)
2 (2 missing values)
mean (±stdev) mpcc_ag 1.5 (±0.73)
2.9 (±1.84)
6.2 (±0.24)
* based on those buildings in which adaptive opportunities were recorded.
As might be expected, mean levels of the perceived control index were lowest in those
buildings with centralized HVAC systems in place and highest in those buildings classified
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 125
as “mixed mode” where benefits of both natural ventilation and air-conditioning were
available for the occupants to use, as and when they saw fit. Naturally ventilated buildings
typically had middle-ranking values on the perceived control index. The difference in mean
pcc_ag between centralized HVAC and NV buildings was statistically significant (t = 5.52, df
= 104, p < 0.001).
Admittedly we are uncertain that the index of perceived control (PCC_AG) developed in this
study conforms to all the assumptions necessary for linear regression. Bearing this caveat
in mind, preliminary investigations of its relationships with thermal perceptual variables were
performed. Several of the variables from earlier sections of this chapter represented logical
candidates for these exploratory analysis.
All Buildings
-0.3
-0.1
0.1
0.3
0.5
0.7
0.9
1.1
1.3
1.5
1.7
0 1 2 3 4 5 6 7mean percieved control index (pcc_ag)
ther
mal
sen
sitiv
ity
(mea
n gr
ad_t
op)
grad_top = 0.36 - 0.02 * pcc_agR2 = 0.03, p = 0.1574
All Buildings
-0.3
-0.1
0.1
0.3
0.5
0.7
0.9
1.1
1.3
1.5
1.7
0 1 2 3 4 5 6 7
mean percieved control index (pcc_ag)
ther
mal
sen
sitiv
ity
(mea
n gr
ad_s
et)
grad_set = 0.17 + 0.004 * pcc_ag
R 2 = 0.01, p = 0.5392
Figure 3.30: Regression analysis between thermal sensitivity and mean perceived control index (pcc_ag)
The adaptive model predicts that occupants of buildings in which there is a high level of
perceived control over thermal conditions will be less critical of indoor climatic conditions
than those in tightly regulated environments. Translating this hypothesis to the RP-884 meta-
analysis, Figure 3.30 plots each building’s thermal sensitivity statistic (dependence of
thermal sensation votes on either operative or standard effective temperature indices) in
relation to the building’s perceived control index score. The failure to reach statistical
significance in both the operative temperature and standard effective temperature index
graphs of Figure 3.30 lends no support to the adaptive hypothesis.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 126
Another extension of this perceived control hypothesis predicts that buildings with high
degrees of occupant control would score higher ratings on thermal acceptability than those
with low levels of control. Figure 3.31 fails to support this hypothesis since there is a
complete absence of any relationship between buildings’ direct thermal acceptability ratings
and their perceived control index score.
All Buildings
0
10
20
30
40
50
60
70
80
90
100
0 1 2 3 4 5 6 7
mean pcc_ag
TS
A (
% a
ccep
tabl
e)
tsa = 82.16 - 0.90 * pcc_ag
R2 = 0.03, p = 0.1904
Figure 3.31: Regression analysis between direct thermal acceptability rating of buildings (f_tsa_2) and their mean level of perceived control (pcc_ag)
Another corollary of the adaptive thermal control hypothesis is that occupants of buildings in
which there is high thermal controllability should be less likely to request a change of
temperature when presented with the thermal preference questionnaire item (MCI). Testing
this prediction with the RP-884 database can be done by tallying the percentage of each
building’s occupant sample who voted for either warmer or cooler temperatures (100 -
F_MCI_2). The thermal control hypothesis predicts that this percentage should decrease in
buildings where the degree of perceived control increases, but as seen in Figure 3.32, the
RP-884 database offers no empirical support for this hypothesis.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 127
All Buildings
0
20
40
60
80
100
0 1 2 3 4 5 6 7mean pcc_ag
% w
antin
g ch
ange
(1
00 -
f_m
ci_2
)
(100 - f_mci_2) = 47.33 + 2.02 * pcc_ag
R2 = 0.05, p = 0.0237
Figure 3.32: Regression analysis between the percentage of building occupants requesting a change in temperature (100- f_mci_2) and the mean level of perceived control (pcc_ag) for the building.
3.3.3. Building occupancy types - offices, residential and industrial
Another corollary of the adaptive hypothesis is that the thermal perception of a particular set
of thermal environmental factors is determined, in part, by the physics of the body’s heat
balance, but also by the functional context of the building setting. That is, perception of a
given state of body heat balance may differ, depending on the setting, because the
occupants’ expectations are context specific and as such, not directly transferable from, say,
the office setting to residential. In order to explore these issues in the RP-884 database,
building function was classified within the database using the information supplied by the
original researchers (Appendix C). A simple three-fold classification consisted of 1)
residential, 2) office, and 3) industrial.
Table 3.18 presents the summary statistics for each of the main thermal environmental
parameters across all three functional classes of building in the RP-884 database.
Obviously the overwhelming majority of buildings in the database were offices and so the
analyses and conclusions developed in earlier sections of this chapter apply primarily to this
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 128
class of building. However, the small sample of residential buildings summarized in Table
3.18 permit some comparisons to be drawn with office buildings. Firstly, the percentage of
physical measurements of indoor climates actually meeting the ET* recommendations of
ASHRAE Standard 55-1992 was remarkably low for the 16 residential buildings in the
sample, ranging from an average of 6% in summer to 21% in winter. These low compliance
levels mainly resulted from the high mean indoor summer temperature of 30°C and low
indoor temperature means of 19°C in winter. Table 3.18 also indicates that mean indoor air
speeds were generally higher in residential buildings compared with office and industrial
settings, and they also showed a much larger seasonal variation in the residential cases.
While mean metabolic rate estimates indoors remained relatively constant across
residential and office settings at about 1.2 met units, they were noticeably higher in the small
number of industrial buildings included in the RP-884 sample, with means ranging between
2 and 2.5 met units. The seasonal swing in mean building occupant thermal insulation levels
was relatively small in the case of office and industrial buildings, amounting to less than 0.2
clo units. However, there was a much larger seasonal adjustment of insulation means
across the residential buildings in the sample, suggesting that clothing adjustment
represents a more powerful adaptive response in the home than in the workplace.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 129
Table 3.18: Summary of the thermal environmental conditions in three classes of building included in the RP-884 database.
residential summer winter
offices summer winter
industrial summer winter
number of buildings in sample$
12
4
98
38
4
4
mean (±stdev) * operative temp (°C)
30.2 ±0.932
18.8 ±4.86
24.3 ±2.07
22.6 ±0.74
22.6 ±1.40
20.4 ±1.64
mean (±stdev) relative humidity (%)
43.9 ±17.0
45.9 ±13.3
53.2 ±9.1
32.8 ±10.5
48.9 ±3.4
43.7 ±8.3
mean (±stdev) % compliance with ASHRAE Standard 55@
6.4 ±7.4
20.9 ±13.0
55.8 ±30.6
86.5 ±16.6
0 ±0
0 ±0
mean (±stdev) air speed (m s-1)
0.31 ±0.10
0.15 ±0.04
0.13 ±0.07
0.08 ±0.03
0.06 ±0.00
0.06 ±0.002
mean (±stdev) insulation (clothes+chair) (clo)
0.58 ±0.16
1.34 ±0.16
0.70 ±0.075
0.89 ±0.16
0.66 ±0.06
0.82 ±0.08
mean (±stdev) metabolic rate (met)
1.20 ±0.08
1.12 ±0.04
1.20 ±0.10
1.17 ±0.05
2.54 ±0.08
2.14 ±0.573
* Mean and stdev figures quoted are averages across the n buildings in the table’s cell $ Note that in some studies large samples across several buildings have been treated as one building due to
the way the data were originally supplied to the RP-884 database @ Percentage of physical observations within each building falling between ASHRAE Standard 55-92 ET*
limits for the relevant season
Having summarized the physical environmental and behavioral factors in three classes of
building in Table 3.18, the main task of Table 3.19 is to summarize the subjective thermal
responses to those indoor climatic conditions, again for residential, office and industrial
settings. It appears that the samples of residential building occupants were, on average,
less than half as sensitive to indoor temperature as the office building samples, since the
gradient of their thermal sensation votes with respect to indoor operative temperature was
about one vote per every 3~5 K change in temperature. In comparison the statistic from the
sample of office buildings was closer to one sensation unit to every two degrees. Another
noteworthy comparison between building function in Table 3.19 concerns acceptability
ratings of buildings. Despite the very low level of ASHRAE Standard 55 compliance in the
residential buildings in the database (Table 3.18), their acceptability ratings, at least in
summer, were not appreciably lower than those registered in office buildings where the
Standard 55 compliance levels were a good deal higher. Even in winter the acceptability
ratings in residential sample buildings dropped only about 10% below the office buildings’
average rating, whereas the ASHRAE Standard compliance levels dropped by over 60%
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 130
from office to residential settings. The implication of these comparisons is clearly that
contextual factors have a strong bearing on how a given set of indoor thermal environmental
parameters will be perceived by the occupants.
Table 3.19: Summary of the subjective thermal responses across the three classes of building included in the RP-884 database.
residential summer winter
offices summer winter
industrial summer winter
number of buildings in sample$
11
3
66
21
1
0
mean ±stdev thermal sensitivity (i.e. regression gradient (sensation vote/deg K top)
0.20
±0.130
0.13
±0.066
0.47
±0.242
0.44
±0.197
0.35 n.a.
n.a. n.a.
mean ±stdev thermal neutrality (°C top)
25.66 ±2.176
24.41 ±1.356
24.11 ±1.627
22.03 ±1.335
19.23 n.a.
n.a. n.a.
mean ±stdev % casting acceptable thermal sensation votes (i.e. -1.5 < ASH < +1.5)
81.21
±8.756 n = 12
66.70
±38.012 n = 4
79.17
±11.239 n = 98
78.00
±16.046 n = 38
28.78
±17.494 n = 4
55.20
±11.827 n = 4
* Mean and stdev figures quoted are averages across the n buildings in the table’s cell $ Note that in some studies large samples across several buildings have been treated as one building due to
the way the data were originally supplied to the RP-884 database. Also, sample size is based only on statistically significant regression models, except where otherwise indicated (i.e. n=...)
n.a. “not applicable”
3.4. Summary of basic results
This chapter has presented a complex array of findings, exploring different thermal indices,
different dimensions of subjective comfort, the effects of different seasons and climates,
different modes of indoor climate control, and different patterns of building occupancy. This
final section summarizes and interprets the key findings in relation to the adaptive
hypothesis of thermal perception. This synthesis provides the starting point for developing
more complex adaptive models in the next chapter.
3.4.1. Summary of thermal sensation, acceptability and preference
Subjective thermal comfort research has been unfortunately complicated over the last thirty
or forty years with the adoption of several different constructs of thermal perception. This
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 131
chapter dealt with three of these -- thermal sensation, thermal acceptability and thermal
preference. Sensation appears to be the most universally used version of questionnaire
scale, and certainly the most ubiquitous within the RP-884 database. Consequently the
more complex and abstract statistical analyses in this project were necessarily confined to
this expression of thermal comfort. However, there was a useable quantity of data on
thermal acceptability and preference within database as well, permitting several
observations to be made about the semantic similarities and differences between all three
constructs and the implications for practical applications.
A very clear observation that emerges from the RP-884 analyses of direct assessments of
thermal acceptability is that building occupants’ responses to direct questions such as this:
“Is the thermal environment in this building at the moment acceptable to you or not?”
bear virtually no relationship to the objective, physical conditions prevailing within the
building at the time of the questionnaire. Evaluations of RP-884 database buildings’ indoor
climatic quality in terms of its compliance with the relevant summer or winter temperature
prescriptions of ASHRAE Standard 55 were completely dissociated from the direct
acceptability ratings of those same buildings by their occupants. We therefore regard
questionnaire items on direct thermal acceptability as being too ambiguous and vague to be
of any practical value in thermal comfort research or practice.
While direct ratings of thermal acceptability for indoor climates may not be particularly useful,
there remains a practical need for information about the range of temperatures which can be
regarded as acceptable for a given building in a specific climatic context. Accepting
Fanger’s (1970) assumption that a mean sensation vote of ±0.85 corresponds with 80%
thermal acceptability (or ±0.50 corresponds with 90%), it was possible in Section 3.1.3.4 to
extract from the database ranges of acceptable temperatures within each of the sample
buildings. ASHRAE Standard 55 suggests operative temperature ranges between 3K and
3.5 K. The RP-884 database, on the other hand, indicated that only a 2.5 K range was
acceptable, on average, within HVAC buildings. In NV buildings, however, the 90%
acceptable range extended significantly further, with an average of 5 K. This stretched to 7
K for the less stringent 80% acceptability criterion.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 132
The actual range of acceptable operative temperatures within any particular building was
found to depend to a large extent on the degree of indoor climatic variability measured within
that building (r=+0.66). This relationship suggests that if, through prior experience,
occupants of a building come to expect considerable thermal variability, the range of
temperatures regarded as acceptable will extend accordingly.
Compared to direct thermal acceptability ratings, thermal sensation rating scales showed a
much more consistent pattern of association with indoor thermal environmental indices.
Correlations within each building in the database were statistically significant in the majority
of cases in Appendix A, and this permitted the derivation of thermal neutralities wherever the
sample of building occupants was large enough. Thermal neutrality is defined as that value
of a thermal index (TOP, ET, SET or PMV) corresponding with a mean thermal sensation
rating of “neutral” by the building’s sample of occupants. Assuming that neutrality is
synonymous with the “optimum thermal condition” for a particular building, it should be more
useful than direct acceptability ratings as a basis for application and practice.
The temperatures which building occupants felt to be neutral were broadly similar in both
HVAC and NV buildings, coming in at about 24°C in summer and 22.5°C in winter (TOP or
ET). These figures approximate the centre of ASHRAE Standard 55’s summer and winter
comfort zones. Neutrality defined in terms of the fully developed heat balance index such as
SET also fell within the same range. Thermal neutrality depended on mean temperatures
within both HVAC and NV buildings, but the rate of change of neutrality with respect to mean
building operative temperature was twice as steep in NV buildings as it was in HVAC
buildings. This finding suggests that occupants of NV buildings were twice as adaptable in
terms of making themselves feel neutral than their counterparts in HVAC buildings.
Fanger’s PMV model seemed to be reasonably accurate at predicting building neutralities
across the whole sample of buildings, with an average prediction error less than half a
degree (compared to observed neutralities). However, the standard deviation of the
prediction error between buildings was quite high at 3.8 K. This suggests that, while the
model worked well across a large sample of buildings, its predictions within any single
building could be significantly wrong. Assuming that the quality of input data in the RP-884
database is of a uniformly high standard (Class 1 and II studies only), the explanations for the
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 133
PMV model’s building-specific prediction errors must lie in non-thermal factors beyond the
human heat balance. For example, the average prediction error in NV buildings was nearly
a full degree, and its between-building standard deviation exceeded 5 K, suggesting that
contextual factors degraded the model’s predictive powers.
Preferred temperatures (as distinct from neutral or acceptable temperatures) could only be
derived within only a subset of the RP-884 building sample. Preferences were found to
occur within the 21~27°C range in most buildings. The average semantic discrepancy
between neutral and preferred temperatures in buildings was generally within half a degree.
While the sign and magnitude of the semantic discrepancy was unrelated to mean warmth
within naturally ventilated buildings, Section 3.1.5 reported a significant tendency for
neutrality to diverge away from preferred temperature within the sample of HVAC buildings
(r=+0.50) as mean temperatures within buildings departed from 22.5°C.
3.4.2. Summary of thermal sensitivity and behavioural thermoregulation
The linear dependence of thermal sensation votes and indoor climate showed a complex
pattern of differences between HVAC and NV buildings, and also between the various
indices of indoor climate (Section 3.1.1). Using the simpler indices such as TOP and ET,
we found that persons in centrally controlled HVAC buildings were, on average, more than
twice as sensitive to changes in temperature as their counterparts in naturally ventilated
buildings. However, this heightened sensitivity diminished when the more complex heat-
balance indices of warmth such as PMV and SET were used, with a sensation category
having a fairly constant temperature width of about four degrees. One interpretation is that
occupants of naturally ventilated buildings behaviorally regulate their heat balance with
clothing and air speed adjustments such that they can accommodate wide variations in
temperature indoors without adverse impacts on thermal sensation -- that is, they are
actively thermoregulating their sensations. In contrast, occupants of centrally heated and air-
conditioned buildings seem less adaptive behaviorally, and as a result their thermal
sensations appear more sensitive to excursions of indoor temperature away from average,
expected set-points.
This interpretation is further supported by the clear relationships between behavioral factors
(clothing and air speed) and indoor temperature (Section 3.1.6). While the seasonal mean
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 134
insulation (clothes plus chair) levels were broadly similar in NV and HVAC buildings, there
was considerably greater variability within and between the NV buildings. Also, there was a
much higher correlation between insulation and indoor temperature in NV buildings (r= -
0.81) compared to HVAC buildings (r= -0.42). The data for indoor air speeds lends
additional support to this interpretation, with the summer average being twice as high in NV
as in HVAC buildings, and the variability within NV buildings also being greater.
Furthermore, the closer correlation between indoor air speed and indoor temperature in NV
buildings (r=+0.73) compared with HVAC buildings (r=+0.58) reinforces the conclusion that
occupants of naturally ventilated buildings were behaviorally more active in thermoregulating
their thermal sensations than were their counterparts in HVAC buildings.
3.4.3. Summary of the effects of outdoor climate on thermal perception indoors
The temperatures found to be neutral within both HVAC and NV buildings varied, depending
on season, with significantly warmer neutralities (defined in terms of operative temperature)
occurring in summer compared to winter. These seasonal differences became less
consistent as the thermal index used to define neutrality increased in complexity (PMV and
SET), but this may simply result from the climatologically inaccurate definition of “summer”
and “winter” applied throughout the database.
Parameterizing outdoor climate simply as the mean of daily maximum and minimum
effective temperatures (in shade) provided a more rational basis for exploring these effects
in Section 3.2.1. Thermal neutrality within buildings was found to correlate positively
(r=+0.65) with mean outdoor ET. While the strength of correlation was roughly comparable
between HVAC and NV buildings, the slope of the linear relationship was not -- indoor
neutrality was about twice as responsive to outdoor temperature in naturally ventilated
buildings compared to air-conditioned. This difference suggests that much of the
adaptability observed in free-running buildings, described earlier as being driven by
expectations of warmth indoors, may in fact be driven by outdoor climate. Obviously indoor
and outdoor temperatures are highly correlated in naturally ventilated buildings (r=+0.91,
compared to r=+0.53 in HVAC buildings), so the temptation to include both in a multiple
regression model of thermal neutrality must be resisted if the stability of regression
coefficients is to be maintained.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 135
As for explaining why thermal comfort adaptability might be related to outdoor climate, the
role of behavioral adjustments was the first place to look (Section 3.2.4). In particular, mean
clothing insulation worn inside buildings, both HVAC and NV, was found to correlate
negatively with mean warmth in the outdoor environment (r=-0.63). Mean air speeds inside
buildings were also found to be, correlated with outdoor warmth, but much more so in the
case of NV buildings (r=+0.80 compared to r=+0.44 in HVAC). It was also clear that the
range of mean air speeds found within naturally ventilated buildings (often exceeding
0.4~0.5 m/s) was much wider than in HVAC buildings where it rarely exceeding the 0.2 m/s
mandated in ASHRAE Standard 55 (1992).
The combined effect of these behavioral thermoregulatory processes and their relationships
with outdoor climate were examined in 3.2.1.4 where building neutralities predicted by the
heat-balance index PMV were regressed on mean outdoor ET. The simplistic description of
the PMV index as a “static” model throughout much of the adaptive comfort literature
(reviewed in Chapter 1) was clearly not supported in this analysis, because observed
regression equations were statistically significant and positive in both HVAC and NV
samples.
3.4.4. Summary of the effects of contextual factors and perceived control
The RP-884 index of perceived thermal control comprised a check-list of specific adaptive
opportunities and their relative efficacy which we applied to each of the buildings in the
database. The index clearly differentiated mixed-mode buildings from naturally ventilated
buildings as affording their occupants the greatest degree of thermal control, largely due to
their provision of both thermostats and operable windows. Naturally ventilated buildings
came up second in average control index rankings, while the centrally-controlled HVAC
buildings scored worst on the index. Despite the ability of the index to differentiate the three
type of building in the RP-884 database, we found it had no correlation with thermal
acceptability, sensitivity or preferences.
Rather than interpreting this as a categorical negation of the role of perceived control in
thermal perception, we think there are at least two alternative explanations:
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 136
1. The validity of the index itself is flawed. The perceived control scale was constructed very
simplistically, mainly due to the nature of the raw data supplied to the RP-884 database.
2. Alternatively, the effects of perceived control may not have such a simple and direct
relationship with thermal perception. The constructs of perceived control and adaptive
opportunity within buildings may in fact exert more complex effects on thermal perception,
and as a result, be statistically significant once other dimensions of indoor and outdoor
climate have been taken into account. The possibility of complex, interactive effects of
the pcc_ag index on thermal perception will be explored further in Chapter 4.
The functional classification of RP-884 sample buildings into office, residential and industrial
uncovered sharp differences in the basic indoor thermal environmental parameters such as
air speed and temperature. The three classes of building were also clearly differentiated in
terms of their compliance with effective temperature index limits within ASHRAE Standard
55. For example, a majority of the observations made inside office buildings, regardless of
whether they were air-conditioned or not, complied with the ASHRAE Standard 55’s ET*
limits, whereas the typical residential or industrial building scored less than 20% compliance
with the standard. We also observed distinct differences in the degree of behavioral
thermoregulatory adjustment made by residential building occupants compared to office
workers. For example, seasonal clothing insulation contrasts were sharper in the residential
as opposed to office setting.
Despite these obvious differences in physical and behavioral features of indoor climate for
office and residential buildings in the sample, we couldn’t discern sharp differences in
occupant evaluations of the buildings’ indoor climatic quality. Despite their relatively poor
performance on the objective physical indoor climatic criteria, occupants’ thermal
acceptability ratings for residential buildings were comparable to those within office
buildings. The strength of this contextual effect on subjective response is no doubt part of
the explanation for the lack of any statistical correlation between thermal acceptability
responses and indoor or outdoor climatic indices, as noted in Section 3.4.1 of this chapter.
ASHRAE RP-884 Final Report
Basic Results page MRL Australia 137
ASHRAE RP-884 Final Report
__________________________________________________________________________________ Towards Adaptive Models page MRL Australia 139
CHAPTER 4 -- TOWARDS ADAPTIVE MODELS
The preceding chapter presented field evidence for the effects of indoor and outdoor
climatic factors on the way building occupants perceive the environments provided by their
buildings. Contextual factors such as whether the building is residential or work-place, and
how much adaptive opportunity it affords were also investigated. The aim of this chapter is
to develop these themes into adaptive models of thermal comfort, relating them back to
previous research on the topic, as reviewed in Chapter 1. These adaptive models will form
the basis of a variable temperature standard for indoor climate to be proposed in the next
chapter.
The reader should note that we have tried to make the terminology of equations in this and
subsequent chapters more descriptive than the nomenclature of earlier chapters.
4.1. The semantics of thermal comfort
Chapter 3 demonstrated that the indoor temperature regarded by building occupants as
“neutral” did not always coincide with that which they rated as most “acceptable” or
“preferable.” Evidence was presented for a “semantic artefact” which caused neutrality
(derived from thermal sensation) to be displaced to the right of preference (warmer) in hot
climates, and to the left of preference (cooler) in cold climates. In other words, in hot
climates people preferred a thermal sensation slightly cooler than neutral, while in colder
climates they preferred to feel slightly warmer than neutral.
Earlier researchers have found similar semantic effects -- de Dear et al. (1991a) recorded a
group-mean thermal sensation of -0.33 for their Singaporean climate chamber subjects
while they were seated in their self-determined preferred temperature (i.e. they preferred to
feel cooler than neutral). This is the equivalent of one whole degree (K) in semantic offset for
the clothing, metabolic rate and air speed in question. Oseland (1994a,b) also reported
semantic discrepancies between preference and thermal sensations, finding that they were
stronger in their winter study where subjects decidedly preferred thermal sensations that
were slightly warmer than neutral. The extent to which culture and climate affect people’s
ASHRAE RP-884 Final Report
__________________________________________________________________________________ Towards Adaptive Models page MRL Australia 140
thermal preferences, and the semantics they use to describe them, has also been discussed
at length by McIntyre (1978a, 1978b, 1982).
A test of this semantic artefact hypothesis within the RP-884 database appeared in Figure
3.24 as a set of graphs relating the discrepancy (discrep) between neutrality and preference
within each building to the mean level of effective temperature in outdoor climate. There we
found a significant linear correlation of r=+0.62 between discrep and mean outdoor effective
temperature (dayavet) within the HVAC (and a few mixed-mode) buildings in the database.
The linear equation indicated that neutrality and preference coincided only in those HVAC
(and mixed-mode) buildings located in climates where the mean outdoor effective
temperature was 13.6°C.
semantic effect = -0.95 + 0.07 * mean outdoor ET* for HVAC buildings eq. 4.1
In climates warmer than this, indoor preference became progressively cooler than neutrality,
while in regions where mean outdoor effective temperature fell below 13.6°C, preferred
temperature was warmer than neutral temperature. Clearly this semantic effect needs to be
accounted for when we develop variable temperature standards for HVAC buildings in the
next chapter.
In contrast to the situation just described for HVAC (and a few mixed mode) buildings, there
was no empirical evidence in the RP-884 database for a semantic effect within naturally
ventilated buildings (Figure 3.24). Exactly why people use words like “slightly warm” or
“slightly cool” differently in different types of buildings remains unclear at this stage.
Whatever the interpretation, it seems reasonable to develop a variable temperature
standard for naturally ventilated buildings exclusively on the basis of thermal neutrality, as
derived from rating scales such as the ASHRAE and Bedford 7-pt scales, ignoring the
semantic offset altogether.
The implications of the semantic effect on the HVAC building adaptive model can be
depicted graphically in Figure 4.1. There the “adaptive model” represents the thermal
neutrality function with respect to outdoor temperature from chapter three minus the
semantic effect just discussed.
ASHRAE RP-884 Final Report
__________________________________________________________________________________ Towards Adaptive Models page MRL Australia 141
buildings with centralized HVAC
-5
0
5
10
15
20
25
30
-5 0 5 10 15 20 25 30 35
mean daily outdoor effective temperature (oC)
com
fort
tem
per
atu
re (
oC
)
-5
0
5
10
15
20
25
30
sem
anti
c ef
fect
(K)
neutrality
semantic effect
adaptive modelincluding semantics
Figure 4.1: An adaptive model for HVAC buildings that accounts for the semantic offset between neutrality and preference.
4.2. Comparison of RP-884 models with earlier adaptive model publications
Indoor Climate: As noted in Chapter 1 (literature review), Humphreys (1975, 1978; 1981)
published various statistical models of the adaptive dependence of indoor thermal neutrality
on mean indoor and outdoor temperature. His statistical analysis of thirty six Class III studies
from various countries around the world revealed a clear dependence of thermal neutralities
(roughly equivalent to neut_top in RP-884 nomenclature) on the mean levels of air or globe
temperature (roughly equivalent to top in RP-884) recorded within the buildings (Humphreys,
1975):
neutrality = 2.56 + 0.83 * operative temperature (r=+0.96) eq 4.2
The RP-884 adaptive model that is most comparable to this equation of Humphreys’ can be
found in Figure 3.1 of the preceding chapter, where thermal neutrality (defined in terms of
operative temperature, neut_top) achieved weaker but still highly significant weighted
regression and correlation with building mean indoor operative temperature (top):
neutrality = 15.34 + 0.35 * operative temperature (r=+0.62) eq 4.3
ASHRAE RP-884 Final Report
__________________________________________________________________________________ Towards Adaptive Models page MRL Australia 142
This RP-884 correlation coefficient can be improved slightly by deletion of all RP-884
industrial buildings and all Class III field studies from the analysis, since these were the clear
outliers throughout the Chapter 3 analyses. With a total of 11,620 subjects inside 98
separate buildings (HVAC plus naturally ventilation) remaining in the analysis, the following
statistically significant adaptive model was derived:
neutrality = 12.93 + 0.44 * operative temperature (r=+0.68) eq 4.4
The RP-884 model in eq 4.4 indicates that thermal neutrality is barely half as sensitive to
mean building temperature compared to Humphreys’ model in eq 4.2. The RP-884 version
of the model indicates that neutral temperatures indoors increase by one degree for each
two-and-a-third degrees increase in mean indoor operative temperature, whereas the
comparable figure in Humphreys’ eq 4.2 approaches unity (one degree per 1.2 K of indoor).
Possibly the difference can be accounted for by the varying compositions of the two building
samples. Section 3.1.2.1 in the last chapter demonstrated a significant difference in the
sensitivities for HVAC and naturally ventilated buildings, so a different balance in the
composition of the all-building samples (HVAC plus natural ventilation) that were used to
generate eqs. 4.2 (Humphreys) and 4.4 (RP-884) would logically affect the resulting models’
gradients.
Outdoor Climate: Auliciems (1983) reanalysed Humphreys’ database of Class III field
research after deleting some suspect data and including new Class III studies that had been
published post Humphreys. The revised database included a total of 52 Class III field
studies. Fortunately the Auliciems (1983) paper included a table of fundamental statistical
data from each study in his database, including thermal neutrality (based on indoor air
temperature), mean monthly outdoor temperature (based exclusively on air temperature), as
well as an indication of whether the buildings in which the studies were conducted had
central HVAC systems or were naturally ventilated (see Appendix G). We have reanalysed
Auliciems’ database for the purpose of comparison with ASHRAE RP-884 and the resulting
regression models are presented below in Figure 4.2.
The models in Figure 4.2 indicate a disparity between the regression models for HVAC and
NV buildings, with the HVAC model having the smaller gradient. The statistical significance
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of this difference was confirmed using the technique described in Kleinbaum et al. (1988)
(T=3.89, df=48, p<0.05). This begins to suggest that people in naturally ventilated buildings
are more connected to the natural swings and cycles in outdoor climate, and their optimum
thermal comfort conditions are more strongly influenced by these experiences.
All Buildings - Auliciems' Data
neut_ta = 0.31 * ( mean month outdoor temp) + 17.6R2 = 0.77
16
18
20
22
24
26
28
30
32
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34
mean monthly outdoor air temperature (oC)
neut
ral i
ndoo
r ai
r te
mpe
ratu
re (
oC
)
Central Heated/HVAC Buildings-Auliciems' Data
neut_ta = 0.19 * (mean month outdoor temp) + 19.0
R2 = 0.48
16
18
20
22
24
26
28
30
32
0 4 8 12 16 20 24 28 32mean outdoor air temperature (oC)
neut
ral i
ndoo
r te
mp
(oC
)
Naturally Ventilated Buildings-Auliciems' Data
neut_ta = 0.52 * (mean month outdoor temp) + 12.3
R2 = 0.89
16
18
20
22
24
26
28
30
32
0 4 8 12 16 20 24 28 32
mean outdoor air temperature (oC)
neut
ral i
ndoo
r te
mp
(oC
)
Figure 4.2: Reanalysis of Auliciems’ (1983) Class 3 field study database (Appendix G) for the effects of outdoor climate on thermal neutrality
The RP-884 adaptive models that are most comparable to Auliciems’ in Figure 4.2 are
those based on the linear relationship between building neutralities and mean outdoor daily
effective temperature (Figure 3.18). Clearly both the RP-884 and Auliciems results support
the adaptive hypothesis inasmuch as both models depict a positive dependence of indoor
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thermal neutrality on temperature outside the buildings surveyed, although the “all buildings”
RP-884 model achieved a lower overall correlation coefficient (r=+0.64 compared to
r=+0.87 in Auliciems). Another difference is that the gradient for the “all buildings” model in
RP-884 (Figure 3.18) was only half that found using the Auliciems database (b=0.16 as
opposed to b=0.31). It should be noted, however, that this may not necessarily mean that the
RP-884 building occupants were that much less climatically adapted than their counterparts
in the Auliciems database -- the divergent gradients could simply be an artefact of the
different indices used to represent outdoor climate in the two databases. Auliciems used
outdoor data based on climatological (30 year mean) monthly outdoor air temperatures,
whereas the RP-884 index was based on observed 2-node effective temperature (ET*)
outdoors. The reason this might have an effect is because the 2-node model’s ET* index
quantifies the incremental thermal impacts of elevated humidity. If the observations from
warm and humid climates in Auliciems’ database were transformed from simple air
temperatures into ET*, they would be non-uniformly displaced to the right along the abscissa
in Figure 4.2, with the effect being most pronounced for the warmer temperatures. These
effects, if incorporated into Auliciems’ database, could be expected to depress his
regression model’s gradient towards the slope found in the RP-884 analysis.
Despite being conceptually comparable, the Auliciems and RP-884 approaches to outdoor
climatic adaptive models have fundamental differences which dissuade us from simply
pooling the two databases together. These include:
• Internal consistency for data going into the RP-884 database was more rigorously
controlled. For example, the dependent variable, thermal neutrality, was recalculated from
raw data by us rather than relying on those published by the original researchers.
• The RP-884 outdoor climatic index (ET*) included humidity effects which, as noted
earlier, were ignored in the Auliciems database.
• The RP-884 indoor climatic index, operative temperature, included mean radiant
temperature effects which would be overlooked by simple air temperature
measurements, as used in the Auliciems database.
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• The unit of analysis in Auliciems’ database was the field study, whereas we analysed data
at the level of individual buildings. It is felt that potentially significant contextual effects
may have been glossed over at the former level of data aggregation.
• The ranges of predictor variable, outdoor climate, covered by the two databases were
significantly different. Centrally heated, ventilated and air-conditioned buildings were
observed in climate zones ranging from mean outdoor temperature -5°C⇔ +33°C in the
RP-884 database, whereas Auliciems’ database only covered the range 0°C⇔ +23°C.
Naturally ventilated buildings were observed in outdoor climates ranging from mean
temperature +5°C⇔ +33°C in the RP-884 database, whereas Auliciems’ database only
covered the range +14°C⇔ +33°C. Therefore, the wider range of climates within the RP-
884 database encourages their extensive application across diverse climate zones
around the world.
Apart from these methodological differences between databases, another factor dissuades
us from calculating an RP-884 multiple regression model for comparison with the Auliceims’
combined indoor-outdoor adaptive model (1983):
neutrality = 9.22 + 0.48 * mean indoor temp + 0.14 * mean outdoor temp eq 4.5
High correlations between the “independent” indoor and outdoor temperature variables
throughout the RP-884 database (weighted Pearson’s r=+0.66, p=0.0001) would render any
regression coefficients within multiple regression models unstable. This would have the
effect of making comparisons with Auliciems’ model in eq. 4.5 unreliable (Michael
Humphreys, pers. com. BRE meeting UK, 1993).
4.3. Comparison of RP-884 models with the PMV “static model”
Section 3.2.1.3 indicated that the so-called “static model” of thermal comfort, PMV,
predicted not-so-static comfort temperatures for the buildings in RP-884’s database. In
particular, building occupants’ behavioral manipulations of heat-balance factors such as
clothing and air speeds showed a systematic dependence on outdoor climate. That is,
clothing insulation decreases in warm climates while air speeds increase. Obviously the
adaptive opportunity for manipulating these parameters is context specific, so comparisons
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between the PMV predictions and RP-884 database observations need to be
disaggregated to HVAC and NV sub-samples.
4.3.1. Comparisons within the centrally conditioned building sample
The “static” versus adaptive comparison for RP-884’s HVAC buildings in Figure 4.3 shows
that comfort temperatures, after correction for semantic effects, have only a moderate
variation (less than 2 K) across a very wide range of outdoor climates (spanning about 40
K). An interpretation of this finding could be that occupants of such buildings have become
finely adapted to the mechanically conditioned and static indoor climates being provided by
centralized HVAC services. The question of “what type is this adaptation?” can be
answered by comparison with the comfort temperatures predicted by the so-called “static”
model (PMV). The two models appear very close together in Figure 4.3, with the
discrepancy being a 0.1 K offset in their Y-intercepts. This discrepancy is neither statistically
nor practically significant. PMV, therefore, appears to have been remarkably successful at
predicting comfort temperatures in the HVAC buildings of RP-884’s database. A corollary
of this finding is that the relatively minor behavioral adjustments to clothing and room air
speeds observed for the occupants of HVAC buildings explain the systematic response in
comfort temperature to outdoor climatic variation, and that these adaptive behaviors are, in
fact, being accounted for by the PMV model.
Against this picture of general agreement between models, a subtle but nonetheless
important distinction between the PMV and RP-884 adaptive models deserves a mention.
The latter were based on thermal sensation data, after being corrected for semantic
artefacts, whereas the PMV model was based exclusively on thermal sensation data without
semantic considerations taken into account. While in practical terms the distinction may
seem trivial, what this means is that PMV successfully predicts optimum comfort
temperatures in field settings despite being intended to predict neutral temperatures. For
these reasons we have labelled the Y-axis in Figure 4.3. as “comfort temperature” instead of
“thermal neutrality.”
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buildings with centralized HVAC
20
21
22
23
24
25
-5 0 5 10 15 20 25 30 35mean daily outdoor effective temperature (
oC)
co
mfo
rt t
em
pe
ratu
re (o C
)
RP-884 adaptive modelwith semantics
"static" model (PMV)
Figure 4.3: Comparison of the RP-884 adaptive model (based on observed neutralities corrected for semantic effects) and the “static” model (based on PMV predictions) for HVAC buildings.
It is interesting to note that this graph so closely matches predictions of PMV with
observations in real HVAC buildings, whereas so many of the earlier thermal comfort field
research papers which we discussed in Chapter 1’s literature review indicated quite the
opposite. Indeed, some of those anomalous papers were from authors who contributed their
raw data to this project’s database. Therefore our success at bringing PMV predictions
into line with observations in HVAC buildings most probably can be attributed to the quality
controls and precautions we took when assembling the RP-884 database, which
transformed, to some extent, the raw data used in the authors’ original analyses. Among the
more important of these were probably:
• setting minimum standards on instrumentation and protocols for data going into the RP-
884 database,
• conversion of all clo estimates throughout the entire database to a single standard
(ASHRAE 55-92),
• inclusion of the thermal insulation effects of the chairs used by subjects (McCullough and
Olesen, 1994),
• recalculation of thermal indices from raw data throughout the entire database with a
consistent software tool (Fountain and Huizenga, 1995),
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• application of a consistent set of statistical techniques to all raw data instead of relying on
different author’s approaches to thermal neutrality, preference and other statistically
derived parameters,
• conducting the meta-analysis at the appropriate scale of statistical aggregation, namely
the individual building.
We are therefore led to the conclusion that Fanger’s PMV model is, in reality, an “adaptive”
model which is suitable for application as it was initially proposed back in 1970 by Fanger
himself; as an engineering guide in HVAC buildings the world over1. The main sticking point
with its application in predictive mode before a building is constructed, or occupied, is that it
is unusual to have detailed observations on mean clo values or air speeds within a building
at the design stage. The practical solution here is to seek further guidance from the RP-884
database. We know from Figure 3.25 that the thermal insulation (in clo units, clothes plus
chair) applicable to PMV calculations is highly correlated with mean outdoor effective
temperature (dayavet):
thermal insulation = 0.93 * e-0.013*(mean outdoor ET*) (r= +0.80) eq 4.6
Figure 3.27 indicates that mean room air speeds (m s-1) within HVAC buildings are also
correlated with mean outdoor effective temperature:
mean room air speed = 0.08 * e+0.014*(mean outdoor ET*) (r= +0.44) eq 4.7
so it seems not unreasonable to anticipate the unknown inputs to PMV simply from a
knowledge of the outdoor weather/climate conditions for the site in question.
An even simpler approach is to directly predict PMV-based neutrality using the linear
regression model depicted in Figure 3.20. In effect this amounts to predicting the aggregate
effects of climate on clothing insulation and room air speeds within HVAC buildings.
1 In the introductory chapter to his book entitled “Thermal Comfort - Analysis and Applications in Environmental Engineering” which introduced the PMV model, Fanger was quite clear that the book, and by implication, the PMV model at its core, were intended for application by the HVAC industry in the creation of “artificial climates” in “controlled spaces.” The generalisation of the PMV model to all spaces intended for human occupancy, HVAC or NV, was a much later development that we disagree with.
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Therefore, the adaptive model for HVAC buildings (noted here as “PMV, adaptive”) reduces
to this simple linear equation (from Figure 3.20):
comfort temperature in HVAC = 22.6 + 0.04 * mean outdoor ET* (r= +0.50) eq 4.8
So, it would appear that the occupants of buildings with centralized HVAC systems have
become adapted to the temperatures that they encounter within their buildings -- generally
within the narrow 22~24°C range. Of course this begs the question of whether or not it is
possible to extend the range of comfort adaptation by deliberately letting indoor HVAC
setpoints more closely track outdoor weather and climatic conditions? We concede that a
purposely designed intervention field experiment on a “real” building, would be the most
appropriate way to test this hypothesis. However, we can draw comparisons with the
naturally ventilated RP-884 sample where the rate of change in thermal insulation with
respect to variations in outdoor climate (Figure 3.25) was significantly greater than in
centrally conditioned buildings. Across the 5⇒ 30°C range of mean outdoor effective
temperatures in Figure 3.25, building occupants’ mean insulation (including chair effects)
varied by about 0.3 clo units in the HVAC sample, whereas the clothing response was more
than double this in naturally ventilated buildings across the same outdoor temperatures. In
short, naturally ventilated building occupants appear to be prepared to take on greater
personal responsibility for maintaining their thermal comfort, when required to. Whether they
would be prepared to do likewise if required in HVAC buildings remains a moot point
deserving further research.
The same line of reasoning can be applied to indoor air velocities in HVAC buildings. We
noted they were confined to very low levels (virtually still air, at <0.2 m s-1) within the RP-884
sample of HVAC buildings, almost regardless of outdoor climate (see Figure 3.27). This
stood in marked contrast to the naturally ventilated sample where within-building mean
velocities went up to 0.4 m s-1 for outdoor mean effective temperatures of about 30°C.
These velocities could possibly be feasible inside centrally conditioned buildings, perhaps
with supplementary air movement in the occupied zone provided by local fans or other
means for individual thermal control. Present-day HVAC building occupants appear
adapted to conditioned, still-air conditions, but they may be willing to more actively regulate
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convective and latent heat losses if the behavioral opportunities to do so were made
available to them and HVAC set-points provided the stimulus (eg allowing for warmer indoor
temperatures during summer).
As noted above, new research is required to establish just how much thermoregulatory
responsibility occupants of HVAC buildings may be prepared to accept. Future field
experimentation may suggest that simply predicting PMV-based neutralities from a
knowledge of mean outdoor temperature, as in eq. 4.8 above, is inappropriate for the
purpose of establishing HVAC set-points. Instead, it may well be more appropriate to first
estimate likely indoor clothing insulation levels and air velocities from equations resembling
those established for the naturally ventilated sample in Figures 3.25 and 3.27 (instead of
the HVAC models presented in eqs. 4.6 and 4.7 above), and then iteratively solving the
PMV model for “neutral” operative temperature. Clearly further field experimentation on
these questions of thermal adaptation in HVAC buildings is required.
4.3.2. Comparisons within the naturally ventilated building sample
Figure 4.4 repeats the “adaptive” versus “static” comparisons for the naturally ventilated
buildings within the RP-884 database. One important departure from the method just
applied to HVAC buildings, however, is the omission of the semantic effect, as discussed in
Section 4.1. This is because we were unable to discern any systematic relationship
between the preferred and neutral temperatures for the naturally ventilated buildings
analysed in Figure 3.24.
The remarkable agreement found between PMV and adaptive models in the HVAC building
sample clearly breaks down in the context of naturally ventilated buildings where the adaptive
model shows a gradient almost twice as steep as the heat-balance PMV model’s. This
divergence tested positive using the Kleinbaum et al. technique (1988) (T=2.43, df=80,
p<0.05). It therefore appears as if behavioral adjustments to body heat balance (i.e.
biophysical effects) account for only about half of the climatic dependence of comfort
temperatures within naturally ventilated buildings. In effect, the PMV model has been
demonstrated to function as a partially adaptive model of thermal comfort in naturally
ventilated buildings.
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However, there still remains the other half of the adaptive effect to be explained. Having
partialled out the effects of behavioral adaptations, we’re left with the physiological
(acclimatization) and psychological (habituation) hypotheses discussed in Chapter 1. There
we noted that effects of acclimatization were not in evidence during climate chamber
experiments on moderate heat/cold stress exposures, so it is not surprising that they failed
to reappear in the field settings analyzed in RP-884. Therefore, by a process of elimination,
we are left with psychological adaptation (i.e. expectation and habituation) as the most
likely explanation for the divergence between field observations and heat-balance (PMV)
predictions.
buildings with natural ventilation (no HVAC)
20
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26
27
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29
30
5 10 15 20 25 30 35mean daily outdoor effective temperature (
oC)
com
fort
tem
per
atu
re (o
C)
RP-884 adaptive model
"static" model (PMV)
Figure 4.4: Comparison of the RP-884 adaptive model (based on observed neutralities in Figure 3.18) and the “static” model (based on PMV predictions) applied to naturally ventilated buildings.
One might wonder why the laboratory-based PMV heat balance model works so well in RP-
884’s HVAC buildings but not so for the NV buildings? Perhaps we can regard the former
as being quite comparable to the climate chamber setting? In both climate chambers and
HVAC buildings the thermal environment is entirely regulated by processes outside the
person-environment feedback loop discussed in Chapter 1. Naturally ventilated buildings,
on the other hand, are much more “interactive,” with adaptive feedback loops being closed
at both behavioral and psychological levels.
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4.4. Adaptive models for acceptable ranges of indoor temperatures
Preceding sections defined some simple adaptive models for predicting optimal comfort
temperatures indoors, but overlooked the question of what sort of temperature
inhomogeneity might be acceptable. We saw in Section 3.1.3.4, particularly in Figure 3.9, a
direct correlation between the range of acceptable operative temperatures within each
building and its internal temperature variability (standard deviation). The relationship
reached statistical significance only for the naturally ventilated buildings in the RP-884
sample where the following linear regression model achieved a correlation coefficient of r=
+0.51:
range of acceptable temperatures = 4.2 + 1.65 * (stdev of indoor temperature) eq 4.9
The failure of a similar model to reach significance within centrally conditioned buildings
reinforces the fundamental difference between the HVAC and natural ventilation contexts
discussed at length throughout in this report. In the naturally ventilated setting, it appears as
if building occupants extend their range of thermal acceptability to accommodate the range
of thermal variation expected within their buildings.
We propose the simple model in eq 4.9 as the adaptive approach to prediction of 80%
acceptable ranges within naturally ventilated buildings. But for many applications it simply
will not be feasible to anticipate the standard deviation of indoor operative temperatures for
a building that is either yet to be built or not fully monitored for any significant length of time.
Therefore a more practical alternative for prescribing acceptable indoor temperature ranges
may be to rely on the RP-884 observations, as described in Table 3.9. In HVAC buildings
the general comfort 80% acceptability criterion corresponded, on average, to a range of two
degrees (K) either side of the optimal comfort temperature. Tightening the acceptability
criterion from 80% to just 90% in RP-884’s HVAC building sample meant a narrowing of the
acceptable range to ± 1.2 K. In either case, the corresponding 80% and 90% ranges
observed in the naturally ventilated RP-884 sample were significantly wider, at ± 3.5 K and ±
2.5 K respectively.
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If these acceptable ranges are going to be applied to adaptive models which predict
optimum indoor temperatures on the basis of outdoor climatic conditions, we need to
address the possibility that the acceptable ranges themselves are also dependent on
outdoor climate. For example, one might speculate that the acceptable range diminishes as
outdoor climate becomes hotter for the simple reason that indoor clothing insulation levels
also decrease with increasing outdoor temperature (see Section 3.2.4.1). A statistical
regression test of this possibility was performed by fitting a regression model to the
dependence of acceptable indoor temperature ranges on outdoor effective temperature,
and the results are reported in Table 4.1. It can be assumed that, if the regression model
turns out to have a statistically insignificant gradient term, the subsample’s mean acceptable
range (as described in Table 3.9) can legitimately be applied across all climate zones. As
seen below in Table 4.1, none of the acceptable range models achieved statistical
significance at the 95% confidence level, regardless of building type nor acceptability level.
Therefore, the variable temperature standards to be proposed in the next chapter can be
based on an optimal temperature predicted from outdoor climate, plus or minus a constant
acceptable temperature range for the building type in question, which does not vary with
climate.
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Table 4.1: Assessment of the dependence of acceptable indoor temperature ranges on outdoor effective temperature.
centrally heated/air-conditioned
buildings
naturally ventilated buildings
number of buildings 108 (3 missing values)
41 (4 missing values)
number of buildings with thermal sensation regression models achieving 95% significance*
63
(58% of total)
33
(75% of total) Mean range of indoor temperatures based on the 80% acceptability criterion (K)
4.1
6.9
Regression model for the dependence of the 80% acceptable temperature ranges on outdoor effective temperature Statistical T-test for the regression gradient Statistical significance of T-test
y=3.08 + 0.05*x
1.81 p>0.05
y=6.28 + 0.03*x
0.36 p>0.10
Mean range of indoor temperatures based on the 90% acceptability criterion (K)
2.4
4.9
Regression Model for the dependence of the 90% acceptable temperature ranges on outdoor effective temperature Statistical T-test for the regression gradient Statistical significance of T-test
y=1.81 + 0.03*x
1.81 p>0.05
y=3.70 + 0.02*x
0.36 p>0.10
* Based on those thermal sensation (ASH) models in Appendix A (y=a + b*TOP) achieving 95% statistical significance or better
The next chapter in this report will summarize this chapter’s adaptive models into a pair of
variable temperature standards - one for application in HVAC buildings and another for
application in the naturally ventilated context.
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CHAPTER 5 - VARIABLE TEMPERATURE STANDARDS
The last remaining task for ASHRAE RP-884 is to propose variable temperature thermal comfort
standards. The statistical analyses and adaptive models in Chapters 3 and 4 were presented separately
for buildings with and without centrally controlled HVAC systems. It seems logical, therefore, to partition
this chapter’s variable temperature standards along the same lines. This distinction between centrally-
controlled HVAC buildings in which individual occupants have little or no control over their immediate
thermal environment, and naturally ventilated buildings in which occupants at least have control over
windows, is a unique feature of the ASHRAE RP-884 project. All thermal comfort standards to date
(see Chapter 1), both extant and proposed, regardless of whether they were based on so-called “static”
or “adaptive” models, have been promulgated as universally applicable across all types of building.
By not differentiating their contexts for application, earlier comfort standards are, in effect, extrapolating
from relationships established in centrally controlled HVAC settings to naturally ventilated contexts, and
vice versa. In contrast, a fundamental tenet of RP-884 has been that the indoor climates found in HVAC
and naturally ventilated buildings are not only quantitatively different, but also qualitatively different, and
as such, they require separate comfort standards.
The reader is requested to regard the two standards in this Chapter as self-contained documents. There
is, therefore, some duplication of definitions and related material across the two standards.
5.1. A variable temperature standard for application in buildings with centrally controlled
HVAC
5.1.1. Purpose
To specify the combinations of indoor space environment and personal factors that will produce thermal
environmental conditions acceptable to a majority of the occupants within centrally heated and air-
conditioned spaces.
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5.1.2. Scope
• The environmental factors addressed are temperature, thermal radiation, humidity, and air speed; the
personal factors are those of activity and clothing.
• It is intended that all of the criteria in this standard be applied together, since comfort in the space
environment is complex and responds to the interaction of all of the factors that are addressed.
• This standard applies to general thermal comfort conditions and excludes local discomforts such as
draft, vertical thermal stratification, and radiant asymmetry.
• This standard specifies thermal environmental conditions acceptable for healthy people at atmospheric
pressure equivalent to altitudes up to 3000 m in indoor spaces designed for human occupancy for
periods not less than 15 minutes.
• This standard does not address such non-thermal environmental factors as air quality, acoustics, and
illumination; nor other physical, chemical or biological space contaminants which may affect comfort or
health.
• This standard is intended for use in design of HVAC-systems, design of buildings, evaluation of
existing thermal environments, building ratings or labelling, and testing of HVAC system performance.
• The standard applies exclusively to indoor environments with HVAC systems over which the
occupants have no control. The occupants of such buildings are presumed to have no option to
open/close windows.
5.1.3. Definitions
adaptive model: A linear regression model that relates indoor design temperatures or acceptable
temperature ranges to outdoor meteorological or climatological parameters. Note that the range of
applicable outdoor climates should be restricted to that appearing on the X-axis of the adaptive model’s
graph (i.e. they should not be extrapolated beyond the range of the regression models’ X-variable).
adaptive opportunity: Buildings provide their occupants with varying degrees of adaptive opportunity or
scope to adjust the internal environment (and themselves) to achieve thermal comfort. Sealed, centrally
air-conditioned office buildings with open-plan floor layouts provide minimal adaptive opportunity, while
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naturally ventilated buildings with operable windows and ceiling fans within small single- or dual-occupant
offices typically afford high degrees of adaptive opportunity.
clo: a unit used to express the thermal insulation provided by garments and clothing ensembles, where 1
clo = 0.155 m2 K/W.
comfort, thermal: that condition of mind which expresses satisfaction with the thermal environment; it
requires subjective evaluation. Optimum thermal comfort is assumed to correspond with a thermal
preference vote of “want no change”
environment, thermal: the characteristics of the environment which affect a person’s heat loss.
environment, acceptable thermal: an environment which at least 80% of the occupants would find
thermally acceptable.
humidity, relative (rh): the ratio of the mole fraction of water vapor present in the air to the mole fraction
of water vapor present in saturated air at the same temperature and barometric pressure; alternatively, it
equals the ratio of the partial pressure (or density) of the water vapor in the air to the saturation pressure
(or density) of water vapor at the same temperature.
insulation, chair: incremental thermal insulation of chairs used by building occupants. The typical office
chair’s clo value is ~0.15 clo units. This effect needs to be included in overall thermal insulation estimates
for the PMV model to yield accurate results.
insulation, clothing (Icl): the resistance to sensible heat transfer provided by a clothing ensemble (i.e.,
more than one garment). It is described as the intrinsic insulation from the skin to the clothing surface,
not including the resistance provided by the air layer around the clothed body; it is usually expressed in
clo units. Clothing worn by people indoors is modified to a great extent by the season and outside
weather conditions. During the summer months, typical clothing in commercial establishments consists of
lightweight dresses, lightweight trousers, short or long sleeved shirts and blouses and occasionally a suit
jacket or sweater. These ensembles have clothing insulation values (Icl) ranging from 0.35 to 0.6 clo.
During the winter season, people wear garments constructed of thicker, heavier (ie. warmer) fabrics and
often add more garment layers to an ensemble. A typical indoor winter ensemble would have an Icl value
ranging from 0.8 to 1.2 clo. Where the outside temperature range does not vary a great deal from season
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to season, people do not change the types of garments they wear year round as much as people who
experience extreme hot and cold climates. The (Icl) provided by clothing ensembles can be estimated by
summing the garment Iclu values as described in ASHRAE Standard 55-92 (1992).
insulation, garment (Iclu): the increased resistance to sensible heat transfer obtained from adding an
individual garment over the nude body. It is the effective increase in overall insulation attributable to the
garment and is usually expressed in clo units.
mean air speed (velocity): arithmetic mean of instantaneous air speed measurements within the occupied
zone, integrated over a period of not less than three minutes (m s-1).
mean monthly (or daily) outdoor effective temperature: Arithmetic average of 6am outdoor ET*
(assumed minimum), and 3pm outdoor ET* (assumed maximum) for a calendar month or particular day.
metabolic rate (met): rate of energy production of the body. Metabolism, which varies with activity, is
expressed in met units in this standard. One met is defined as 58.2 Wm-2 which is equal to the energy
produced per unit surface area of a seated person at rest. The surface area of an average person is about
1.8 m2. In today’s society, most people are occupied with light, primarily a sedentary activity level
corresponding to 1 to 1.6 met. Metabolic activity should be assessed for a period between 30 and 60
minutes before any thermal assessment is made. For more detailed values see ASHRAE Standard 55-
1992, ISO 7730, ISO 8996 or the ASHRAE Handbook of Fundamentals (1993).
neutrality, thermal: the indoor thermal index value (usually operative temperature) corresponding with a
maximum number of building occupants voting “neutral” on the thermal sensation scale.
preference, thermal: a conscious desire for change in one’s thermal state, commonly graded into the
categories, 1“want cooler,” 2 “want no change,” 3“want warmer”; it requires subjective evaluation.
Preferred temperature, i.e. that corresponding with a maximum number of “2” votes, does not necessarily
correspond with thermal neutrality.
PMV: Predicted Mean Vote is a thermal index derived from the heat-balance model of thermal comfort
developed by Fanger (1970). PMV predicts the mean thermal sensation of a large group of subjects
experiencing a thermal environment specified in terms of mean air and radiant temperatures, air speed,
humidity, thermal insulation and metabolic rate.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 159 MRL Australia
PMV, analytic: Predicted Mean Vote index calculated analytically from mean measurements or estimates
of the six primary comfort parameters: mean air and radiant temperatures, mean air speed, humidity,
clothing (+ chair) thermal insulation and metabolic rate.
PMV, adaptive: the RP-884 adaptive regression model that predicts optimum thermal comfort
temperature (thermal sensation corrected for semantics). The name “adaptive PMV” is used for the
model because it predicts essentially the same optimum operative temperature answer as the analytic
PMV approach, but uses mean outdoor effective temperature as the only input instead of the usual four
inputs (clo, met, rh and v) required by the analytic PMV method.
sensation, thermal: a conscious feeling commonly graded into the categories, -3 cold, -2 cool, -1 slightly
cool, 0 neutral, +1 slightly warm, +2 warm, and +3 hot; it requires subjective evaluation. An individual’s
ideal thermal comfort does not necessarily correspond with a thermal sensation vote of “neutral” (zero).
summer: operationally defined as the cooling season; climatologically defined for the purposes of this
standard as having a mean daily outdoor effective temperature of 25oC.
temperature, air (ta): the dry-bulb temperature of the air surrounding the occupant.
temperature, dew point (tdp): [or ambient water vapor pressure (Pa)], the temperature at which moist air
becomes saturated (100% relative humidity) with water vapor (Psdp = Pa) when cooled at constant
pressure.
temperature, mean radiant (tr): the uniform surface temperature of an imaginary black enclosure in which
an occupant would exchange the same amount of radiant heat as in the actual nonuniform space.
temperature, operative (to): the uniform temperature of an imaginary black enclosure in which an
occupant would exchange the same amount of heat by radiation plus convection as in the actual non-
uniform environment. Operative temperature is numerically the average of the air temperature (ta) and
mean radiant temperature (tr), weighted by their respective heat transfer coefficients (hc and hr):
th t h t
h ho
c a r r
c r
= ++
( )( )
which typically equates to the arithmetic average of mean air and radiant temperatures.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 160 MRL Australia
temperature, effective (ET*): the operative temperature (to) of an enclosure at 50% relative humidity
which would cause the same sensible plus latent heat exchange from a person as would the actual
environment.
temperature, optimum operative: the operative temperature that satisfies the greatest possible number of
people at a given clothing and activity level. Due to the semantic offset between preferred and neutral
temperatures, optimum operative temperature in this standard does not necessarily correspond exactly
with thermal neutrality (i.e. optimum temperature is neutrality after correction for semantic offset).
temperature, thermodynamic wet bulb: (also called the Adiabatic Saturation Temperature), that
temperature at which water, by evaporating into air, can bring the air to saturation adiabatically at the
same temperature. The wet bulb temperature measured with an appropriate psychrometer can approach
the thermodynamic wet bulb temperature.
winter: operationally defined as the heating season; climatologically, for the purposes of this standard a
typical winter condition is assumed to have a mean daily outdoor effective temperature of 0oC.
zone, occupied: the region normally occupied by people within a space, generally considered to be
between the floor and 1.8 m above the floor and more than 0.6 m from walls or fixed air conditioning
equipment.
5.1.4. Conditions for an acceptable thermal environment.
The conditions for an acceptable thermal environment shall be based on one of the following three
techniques, listed in descending order of preference:
• the analytic PMV method, as described in ISO 7730 (1994) , if mean clothing and metabolic rates
are known in advance, or
• the adaptive PMV method in which indoor optimum operative temperature is predicted from a
knowledge of outdoor effective temperature using RP-884 regression models, or
• the prescriptive method in which summer and/or winter comfort zones for either 90% or 80% thermal
acceptability levels are selected from the RP-884 psychrometric charts.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 161 MRL Australia
5.1.4.1 Analytic PMV Method
See the detailed procedures for estimation of the optimum temperature for a group of building occupants
described in ISO 7730 (1994). The only departure from the methods described there is the inclusion of
the incremental thermal insulation of the chair into the seated occupants’ overall thermal insulation.
Optimum operative temperature may be predicted by inputting measured or estimated values of insulation
(clothing + chair), metabolic rate, relative humidity, air speed and solving for the unknown operative
temperature by setting PMV = zero. Note that the actual group mean thermal sensation expressed by
building occupants under the optimum operative temperature predicted by this method may not
necessarily equal zero (“neutral”). This is due to the semantic offset between group thermal neutrality and
preference. Therefore PMV equal to zero may correspond with a non-zero mean thermal sensation for
the group of building occupants in question, but they will still be in their optimum operative temperature.
5.1.4.2. Adaptive PMV method
In HVAC situations where the mean thermal insulation (clothing and chairs) and mean air speed cannot be
observed or accurately anticipated, the adaptive PMV method may be applied. Weather data in the form
of mean outdoor effective temperature for the relevant time of year is required. In the absence of current
meteorological observation, published
mean climatological data for the relevant month from the nearest or most relevant weather station may
suffice.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 162 MRL Australia
18.0
20.0
22.0
24.0
26.0
28.0
-5 0 5 10 15 20 25 30 35mean daily outdoor effective temperature (oC)
com
fort
tem
pera
ture
(o C
)
comfort temp. in HVAC = 22.6 + 0.04 * outdoor ET*
80% acceptability lower limit
80% acceptability upper limit
Figure 5.1: The adaptive PMV comfort zone’s optimum and limits for an 80% acceptability level in HVAC premises.
18
20
22
24
26
28
-5 0 5 10 15 20 25 30 35
mean daily outdoor effective temperature (oC)
com
fort
tem
pera
ture
(oC
)
comfort temp. in HVAC = 22.6 + 0.04 * outdoor ET*
90% acceptability upper limit
90% acceptability lower limit
Figure 5.2: The adaptive PMV comfort zone’s optimum and limits for an 90% acceptability level in HVAC premises.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 163 MRL Australia
5.1.4.3. Prescriptive method
Where outdoor meteorological or climatological data are unavailable, the RP-884 prescriptive method
may be used to define acceptable ranges of temperatures. The prescriptions are designed to provide
environments in which minimum levels of thermal acceptability (based on general thermal comfort) can be
selected as either 90% or 80%.
0
5
10
15
15 20 25 30
OPERATIVE TEMPERATURE (oC)
HU
MID
ITY
MIX
ING
RA
TIO
(g
/kg
)
30% rh
19o C Wet Bulb
18o C Wet Bulb
100% rh
70% rh60% rh
50% rh
Winter
Summer
24.7 ET*21.3 ET*
0
5
10
15
15 20 25 30
OPERATIVE TEMPERATURE (oC)
HU
MID
ITY
MIX
ING
RA
TIO
(g
/kg
)
30% rh
19 o C Wet Bulb18 o C Wet Bulb
100% rh
70% rh60% rh
50% rh
Winter
Summer
25.5 ET*20.5 ET*
Figure 5.3: Psychrometric charts showing summer and winter comfort zone prescriptions for 90% acceptability (left panel) and 80% acceptability (right panel)
Operative Temperature. The operative temperature range between which, theoretically, no more than
20% of occupants during light, primarily sedentary activity (� 1.2. met), assuming they wear the same
level of clothing insulation, will find the environment thermally unacceptable is given in Table 5.1. The
acceptable range of operative temperatures and humidities for winter and summer is further defined on the
psychrometric chart of Figure 5.3. The comfort zones are:
a) Winter: to = 20.5oC to 24.5oC at 50% rh for 80% acceptability level.
to = 21.3oC to 23.7oC at 50% rh for 90% acceptability level.
The slanting side boundaries of the winter zones in Figure 5.3 are defined in terms of effective
temperature (ET*) lines and are loci of constant thermal sensations.
b) Summer: to = 21.5oC to 25.5oC at 50% rh for 80% acceptability level.
to = 22.3oC to 24.7oC at 50% rh for 90% acceptability level.
The slanting side boundaries of the summer zones in Figure 5.3 are defined in terms of effective
temperature (ET*) lines and are loci of constant thermal sensations.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 164 MRL Australia
The winter and summer comfort zones overlap in the 22oC to 23oC range. In this region people in
summer dress would tend to approach slightly cool sensation while those in winter clothing would be near
the slightly warm sensation. In reality, the boundaries of each zone are not as sharp as depicted in Figure
5.3 due to inter-individual clothing and activity differences.
Table 5.1: Optimum and acceptable ranges of operative temperature for persons engaged in light, primarily sedentary activity (� 1.2 mets) at 50% relative humidity and mean air speed � 0.15 ms-1. For use in buildings with central HVAC systems.
Description of Icl Operative Temperature Season typical thermal insulation clo optimum
temperature range (90% accept.)
range (80% accept.)
Winter
heavy slacks, long sleeve shirt, sweater and office chair
1.05
22.5 oC
21.3 - 23.7 oC
20.5 - 24.5 oC
Summer
light slacks, short sleeve shirt and office chair
0.65
23.5 oC
22.3 - 24.7 oC
21.5 - 25.5 oC
For infants, certain elderly persons, and individuals who are physically disabled, the lower limits of Table 5.1 should be avoided.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 165 MRL Australia
5.2. A variable temperature standard for application in naturally ventilated buildings
5.2.1. Purpose
To specify the thermal environmental conditions that will be acceptable to a majority of the occupants
within naturally ventilated spaces.
5.2.2 Scope
• The environmental factors addressed are temperature, thermal radiation, humidity.
• It is intended that all of the criteria in this standard be applied together, since comfort in the space
environment is complex and responds to the interaction of all of the factors that are addressed.
• This standard applies to general thermal comfort conditions and excludes local discomforts such as
draft, vertical thermal stratification, and radiant asymmetry.
• This standard specifies thermal environmental conditions acceptable for healthy people at atmospheric
pressure equivalent to altitudes up to 3000 m in indoor spaces designed for human occupancy for
periods not less than 15 minutes.
• This standard does not address such non-thermal environmental factors as air quality, acoustics, and
illumination; nor other physical, chemical or biological space contaminants which may affect comfort or
health.
• This standard is intended for use in design of naturally ventilated buildings and evaluation of existing
thermal environments within such buildings.
• The standard applies exclusively to indoor environments without centralised HVAC systems. Such
buildings are presumed to have operable windows which the occupants have some degree of control
over. They may have some form of heating installed, but it would be controlled by the building
occupants, either individually or in small groups.
• The standard cannot be used to decide when and where to install centralised air-conditioning. While it
may provide useful information in relation to such decisions, the standard cannot be regarded as the
ASHRAE RP-884 Final Report
Variable Temperature Standard page 166 MRL Australia
sole criterion. For example, the adaptive opportunity afforded the occupants of naturally ventilated
buildings should also be borne in mind.
5.2.3. Definitions
adaptive model: A linear regression model that relates indoor design temperatures or acceptable
temperature ranges to outdoor meteorological or climatological parameters. Note that the range of
applicable outdoor climates should be restricted to that appearing on the X-axis of the adaptive model’s
graph (i.e. they should not be extrapolated beyond the range of the regression models’ X-variable).
adaptive opportunity: Buildings provide their occupants with varying degrees of adaptive opportunity or
scope to adjust the internal environment (and themselves) to achieve thermal comfort. Sealed, centrally
air-conditioned office buildings with open-plan floor layouts provide minimal adaptive opportunity, while
naturally ventilated buildings with operable windows and ceiling fans within small single- or dual-occupant
offices typically afford high degrees of adaptive opportunity.
comfort, thermal: that condition of mind which expresses satisfaction with the thermal environment; it
requires subjective evaluation. Optimum thermal comfort is assumed to correspond with a thermal
preference vote of “want no change”
environment, thermal: the characteristics of the environment which affect a person’s heat loss.
environment, acceptable thermal: an environment which at least 80% of the occupants would find
thermally acceptable.
humidity, relative (rh): the ratio of the mole fraction of water vapor present in the air to the mole fraction
of water vapor present in saturated air at the same temperature and barometric pressure; alternatively, it
equals the ratio of the partial pressure (or density) of the water vapor in the air to the saturation pressure
(or density) of water vapor at the same temperature.
mean monthly (or daily) outdoor effective temperature: Arithmetic average of 6am outdoor ET*
(assumed minimum), and 3pm outdoor ET* (assumed maximum) for a calendar month or particular day.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 167 MRL Australia
naturally ventilated: Those premises in which a centralised heating, ventilation and air-conditioning
systems are absent and windows are operable. Some form of heating may be present, but it would
normally be under the control of building occupants, either individually or in small groups.
neutrality, thermal: the indoor thermal index value (usually operative temperature) corresponding with a
maximum number of building occupants voting “neutral” on the thermal sensation scale.
preference, thermal: a conscious desire for change in one’s thermal state, commonly graded into the
categories, 1“want cooler,” 2 “want no change,” 3“want warmer”; it requires subjective evaluation.
Preferred temperature, i.e. that corresponding with a maximum number of “2” votes, corresponds
reasonably well with thermal neutrality in naturally ventilated buildings.
sensation, thermal: a conscious feeling commonly graded into the categories, -3 cold, -2 cool, -1 slightly
cool, 0 neutral, +1 slightly warm, +2 warm, and +3 hot; it requires subjective evaluation. Optimum
thermal comfort corresponds reasonably well with a thermal sensation vote of “neutral” in naturally
ventilated buildings.
temperature, air (ta): the dry-bulb temperature of the air surrounding the occupant.
temperature, mean radiant (tr): the uniform surface temperature of an imaginary black enclosure in which
an occupant would exchange the same amount of radiant heat as in the actual nonuniform space.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 168 MRL Australia
temperature, operative (to): the uniform temperature of an imaginary black enclosure in which an
occupant would exchange the same amount of heat by radiation plus convection as in the actual non-
uniform environment. Operative temperature is numerically the average of the air temperature (ta) and
mean radiant temperature (tr), weighted by their respective heat transfer coefficients (hc and hr):
th t h t
h ho
c a r r
c r
= ++
( )( )
which typically equates to the arithmetic average of mean air and radiant temperatures
temperature, effective (ET*): the operative temperature (to) of an enclosure at 50% relative humidity
which would cause the same sensible plus latent heat exchange from a person as would the actual
environment.
temperature, optimum operative: the operative temperature that satisfies the greatest possible number of
people at a given clothing and activity level. Optimum operative temperature in this standard corresponds
reasonably well with both thermal neutrality and preferred temperature.
zone, occupied: the region normally occupied by people within a space, generally considered to be
between the floor and 1.8 m above the floor and more than 0.6 m from walls or fixed air conditioning
equipment.
5.2.4. Conditions for an acceptable thermal environment.
The conditions for an acceptable thermal environment shall be based exclusively on the adaptive model
(linear regression) approach. The PMV/PPD model is inapplicable to naturally ventilated premises
because it only partially accounts for processes of thermal adaptation to indoor climate. The prescription
of summer and winter comfort zones is inappropriate for this standard because the steep gradient on the
naturally ventilated adaptive model would render climatological definitions of universal “summer” and
“winter” conditions misleading.
The adaptive models in this section can be applied where weather data in the form of mean outdoor
effective temperature for the relevant time of year are available. These need to be calculated from basic
outdoor air temperature maxima (3 pm) and minima
ASHRAE RP-884 Final Report
Variable Temperature Standard page 169 MRL Australia
(6 am), along with coincident humidity. In the absence of current meteorological observations, published
mean climatological data for the relevant month from the nearest weather station may suffice.
16
18
20
22
24
26
28
30
32
5 10 15 20 25 30 35mean daily outdoor effective temperature (oC)
com
fort
tem
p (o C
)
80% acceptability upper limit
80% acceptability lower limit
comfort temp. in NV = 18.9 + 0.255 * outdoor ET*
Figure 5.4: The adaptive comfort zone’s optimum and limits for an 80% acceptability level in naturally ventilated
premises.
16
18
20
22
24
26
28
30
32
5 10 15 20 25 30 35mean daily outdoor effective temperature (oC)
com
fort
tem
p (o C
) 90% acceptability upper limit
90% acceptability lower limit
comfort temp. in NV = 18.9 + 0.255 * outdoor ET*
Figure 5.5: The adaptive comfort zone’s optimum and limits for a 90% acceptability level in naturally ventilated premises.
ASHRAE RP-884 Final Report
Variable Temperature Standard page 170 MRL Australia
The charts in this standard require input of the relevant value of outdoor ET* on the X-axis and then
reading off the optimum comfort temperature, upper and lower acceptable limits on the Y-axis. Choose
either Figure 5.4 or Figure 5.5 depending on whether an 80% or 90% acceptability level is being sought.
ASHRAE RP-884 Final Report
Bibliography page 171 MRL Australia
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Bibliography page 184 MRL Australia
ASHRAE RP-884 Final Report
Appendix A page 185 MRL Australia
APPENDIX A - THERMAL SENSATION AND NEUTRALITY FOR EACH BUILDING IN THE RP-884 DATABASE
ASHRAE RP-884 Final Report
Appendix A page 186 MRL Australia
this plot had only one value
South Wales UK (summer), HVAC building #2
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #4
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #8
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #2
-3
-2
-1
0
1
2
3
15 16 17 18 19 20 21
ET* (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #3
-3
-2
-1
0
1
2
3
15 16 17 18 19 20 21
ET* (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #4
-3
-2
-1
0
1
2
3
15 16 17 18 19 20 21
ET* (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #8
-3
-2
-1
0
1
2
3
15 16 17 18 19 20 21
ET* (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV A
SH
South Wales UK (summer), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
South Wales UK (summer), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
South Wales UK (summer), HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
South Wales UK (summer), HVAC
building #2
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #3
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #4
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
South Wales UK (summer), HVAC building #8
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 187 MRL Australia
South Wales UK (winter), HVAC building #7
-3
-2
-1
0
1
2
3
18 19 20 21 22
Operative Temp (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #1
-3
-2
-1
0
1
2
3
11 12 13 14 15 16 17
ET* (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #5
-3
-2
-1
0
1
2
3
11 12 13 14 15 16 17
ET* (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #6
-3
-2
-1
0
1
2
3
11 12 13 14 15 16 17
ET* (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #7
-3
-2
-1
0
1
2
3
11 12 13 14 15 16 17
ET* (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVACbuilding #1
-3
-2
-1
0
1
2
3
18 19 20 21 22
Operative Temp (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #5
-3
-2
-1
0
1
2
3
18 19 20 21 22
Operative Temp (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #6
-3
-2
-1
0
1
2
3
18 19 20 21 22 23
Operative Temp (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
South Wales UK (winter), HVAC building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
South Wales UK (winter), HVAC building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
South Wales UK (winter), HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
South Wales UK (winter), HVAC building
#1
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #5
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #6
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
South Wales UK (winter), HVAC building #7
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 188 MRL Australia
Bangkok Thailand (summer), HVAV building #1
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), HVAV building #2
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), HVAC building #1
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), HVAC building #2
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Bangkok Thailand (summer), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Bangkok Thailand (summer), HVAC building #1
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5 27.5 29.5 31.5
SET (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), HVAC building #2
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5 27.5 29.5 31.5
SET (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), NV building #3
-3
-2
-1
0
1
2
3
25 26 27 28 29 30 31 32 33 34 35
Operative Temp (degC)M
ean
Vot
e
ashraepmv
Bangkok Thailand (summer), NV building #4
-3
-2
-1
0
1
2
3
25 26 27 28 29 30 31 32 33 34
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), NV building #5
-3
-2
-1
0
1
2
3
25 26 27 28 29 30 31 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), NV building 3
-3
-2
-1
0
1
2
3
25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), NV
building #4
-3
-2
-1
0
1
2
3
25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), NV building #5
-3
-2
-1
0
1
2
3
25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), NV building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Bangkok Thailand (summer), NV building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 189 MRL Australia
Bangkok Thailand (summer), NV building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HBangkok Thailand (summer), NV
building #3
-3
-2
-1
0
1
2
3
24 26 28 30 32 34 36
SET (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), NV building #4
-3
-2
-1
0
1
2
3
24 26 28 30 32 34 36
SET (degC)
Mea
n V
ote
ashraepmv
Bangkok Thailand (summer), NV building #5
-3
-2
-1
0
1
2
3
24 26 28 30 32 34 36
SET (degC)
Mea
n V
ote
ashraepmv
Antioch CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
22.5 23.5 24.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Antioch CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
21.5 22.5 23.5
ET* (degC)
Mea
n V
ote
ashraepmv
Antioch CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Antioch CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
20.5 22.5 24.5 26.5 28.5
SET (degC)
Mea
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
22 24 26 28 30 32
Operative Temp (degC)M
aen
Vot
e
ashraepmv
Jakarta Indonesia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
22 24 26 28 30 32
Operative Temp (degC)
Mae
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
22 24 26 28 30 32
Operative Temp (degC)
Mae
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #5
-3
-2
-1
0
1
2
3
22 24 26 28 30 32
Operative Temp (degC)
Mae
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #7
-3
-2
-1
0
1
2
3
22 24 26 28 30 32
Operative Temp (degC)
Mae
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
22 24 26 28 30 32 34
ET* (degC)
Mea
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
22 24 26 28 30 32 34
ET* (degC)
Mea
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
22 24 26 28 30 32 34
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 190 MRL Australia
Jakarta Indonesia (summer), HVAC building #5
-3
-2
-1
0
1
2
3
22 24 26 28 30 32 34
ET* (degC)
Mea
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #7
-3
-2
-1
0
1
2
3
22 24 26 28 30 32 34
ET* (degC)
Mea
n V
ote
ashraepmv
Jakarta Indonesia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Jakarta Indonesia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Jakarta Indonesia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Jakarta Indonesia (summer), HVAC building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Jakarta Indonesia (summer), HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Jakarta Indonesia (summer), NV building #1
-3
-2
-1
0
1
2
3
28 29 30 31 32 33
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Jakarta Indonesia (summer), NV building #1
-3
-2
-1
0
1
2
3
30 31 32 33 34 35
ET* (degC)M
ean
Vot
e
ashraepmv
Jakarta Indonesia (summer), NV building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Jakarta Indonesia (summer), Mixed building #6
-3
-2
-1
0
1
2
3
26 27 28 29
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Jakarta Indonesia (summer), Mixed building #6
-3
-2
-1
0
1
2
3
26 27 28 29 30 31
ET* (degC)
Mea
n V
ote
ashraepmv
The Jakarta, Indonesian filesdo not have SET as a variable
Jakarta Indonesia (summer), Mixed building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #1
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer), HVAC building #2
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #3
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 191 MRL Australia
Montreal Canada RP-821 (summer),HVAC building #4
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #5
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #6
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #7
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #8
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #9
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #10
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #11
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #12
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
Operative Temp (degC)M
ean
Vot
e
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #1
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #2
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #3
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #4
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #5
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #6
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #7
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 192 MRL Australia
Montreal Canada RP-821 (summer),HVAC building #8
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #9
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #10
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #11
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #12
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
Montreal Canada RP-821 (summer),HVAC building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),
HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #9
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #10
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (summer),HVAC building #11
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 193 MRL Australia
Montreal Canada RP-821 (summer),HVAC building #12
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HMontreal Canada RP-821 (summer),
HVAC building #1
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #2
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #3
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #4
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #5
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #6
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #7
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #8
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)M
ean
Vot
e
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #9
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #10
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #11
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (summer),HVAC building #12
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #1
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #2
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #3
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 194 MRL Australia
Montreal Canada RP-821 (winter),HVAC building #4
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #5
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #6
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #7
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #8
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #9
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #10
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #11
-3
-2
-1
0
1
2
3
20 21 22 23 24 25
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #1
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)M
ean
Vot
e
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #2
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #3
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #4
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #5
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #6
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #7
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #8
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 195 MRL Australia
Montreal Canada RP-821 (winter),HVAC building #9
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #10
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #11
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5
ET* (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
Montreal Canada RP-821 (winter),HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #9
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),
HVAC building #10
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #11
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Montreal Canada RP-821 (winter),HVAC building #1
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #2
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 196 MRL Australia
Montreal Canada RP-821 (winter),HVAC building #3
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #4
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #5
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #6
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #7
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #8
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #9
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #10
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Montreal Canada RP-821 (winter),HVAC building #11
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)M
ean
Vot
e
ashraepmv
Brisbane Australia (summer), HVAC building #1
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #5
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #1
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 197 MRL Australia
Brisbane Australia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #5
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), HVAC building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), HVAC building #1
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29 31
SET (degC)M
ean
Vot
e
ashraepmv
Brisbane Australia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), HVAC building #5
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #1
-3
-2
-1
0
1
2
3
25.5 26.5 27.5 28.5 29.5 30.5 31.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #2
-3
-2
-1
0
1
2
3
25.5 26.5 27.5 28.5 29.5 30.5 31.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #3
-3
-2
-1
0
1
2
3
25.5 26.5 27.5 28.5 29.5 30.5 31.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 198 MRL Australia
Brisbane Australia (summer), NV building #4
-3
-2
-1
0
1
2
3
25.5 26.5 27.5 28.5 29.5 30.5 31.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #5
-3
-2
-1
0
1
2
3
25.5 26.5 27.5 28.5 29.5 30.5 31.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #1
-3
-2
-1
0
1
2
3
25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #2
-3
-2
-1
0
1
2
3
25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #3
-3
-2
-1
0
1
2
3
25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #4
-3
-2
-1
0
1
2
3
25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #5
-3
-2
-1
0
1
2
3
25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), NV building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
Brisbane Australia (summer), NV building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), NV building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), NV building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Brisbane Australia (summer), NV building #1
-3
-2
-1
0
1
2
3
23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #2
-3
-2
-1
0
1
2
3
23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #3
-3
-2
-1
0
1
2
3
23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
Brisbane Australia (summer), NV building #4
-3
-2
-1
0
1
2
3
23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 199 MRL Australia
Brisbane Australia (summer), NV building #5
-3
-2
-1
0
1
2
3
23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #1
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #2
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #3
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #4
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #5
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #6
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #7
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #8
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
Operative Temp (degC)M
ean
Vot
e
ashraepmv
Darwin Australia (summer-dry), HVAC building #1
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #2
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #3
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC
building #4
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #5
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #6
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #7
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 200 MRL Australia
Darwin Australia (summer-dry), HVAC building #8
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-dry), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-dry), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-dry), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-dry), HVAC building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-dry), HVAC building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-dry), HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-dry), HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
Darwin Australia (summer-dry), HVAC building #1
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #2
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #3
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #4
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #5
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #6
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-dry), HVAC building #7
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 201 MRL Australia
Darwin Australia (summer-dry), HVAC building #8
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #8
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #9
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #10
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #11
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #12
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #13
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #14
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #8
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
ET* (degC)M
ean
Vot
e
ashraepmv
Darwin Australia (summer-wet), HVAC building #9
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #10
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #11
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #12
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #13
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (Summer), HVAC building #14
-3
-2
-1
0
1
2
3
20 22 24 26 28 30
ET* (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 202 MRL Australia
Darwin Australia (summer-wet), HVAC building #9
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HDarwin Australia (summer-wet), HVAC
building #10
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-wet), HVAC building #11
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-wet), HVAC building #12
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-wet), HVAC building #13
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-wet), HVAC building #14
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Darwin Australia (summer-wet), HVAC building #8
-3
-2
-1
0
1
2
3
21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #9
-3
-2
-1
0
1
2
3
21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #10
-3
-2
-1
0
1
2
3
21 23 25 27 29 31
SET (degC)M
ean
Vot
e
ashraepmv
Darwin Australia (summer-wet), HVAC building #11
-3
-2
-1
0
1
2
3
21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #12
-3
-2
-1
0
1
2
3
21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC building #13
-3
-2
-1
0
1
2
3
21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Darwin Australia (summer-wet), HVAC
building #14
-3
-2
-1
0
1
2
3
21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 203 MRL Australia
Melbourne Australia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #1
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5 27.5 29.5
ET* (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5 27.5 29.5
ET* (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5 27.5 29.5
ET* (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5 27.5 29.5
ET* (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Melbourne Australia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Melbourne Australia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Melbourne Australia (summer), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
Melbourne Australia (summer), HVAC building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33
SET (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #2
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33
SET (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC building #3
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33
SET (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), HVAC
building #4
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33
SET (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), NV building #1
-3
-2
-1
0
1
2
3
10 15 20 25 30 35
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), NV building #2
-3
-2
-1
0
1
2
3
10 15 20 25 30 35
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), NV building #3
-3
-2
-1
0
1
2
3
10 15 20 25 30 35
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 204 MRL Australia
Melbourne Australia (summer), NV building #1
-3
-2
-1
0
1
2
3
12 14 16 18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), NV building #2
-3
-2
-1
0
1
2
3
12 14 16 18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), NV building #3
-3
-2
-1
0
1
2
3
12 14 16 18 20 22 24 26 28 30 32
ET* (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), NV building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Melbourne Australia (summer), NV building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Melbourne Australia (summer), NV building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Melbourne Australia (summer), NV building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), NV building #2
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Melbourne Australia (summer), NV building #3
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
SET (degC)M
ean
Vot
e
ashraepmv
Ottawa Canada (summer), HVAC building #1
-3
-2
-1
0
1
2
3
18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #2
-3
-2
-1
0
1
2
3
18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #3
-3
-2
-1
0
1
2
3
18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #4
-3
-2
-1
0
1
2
3
18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #1
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #2
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #3
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 205 MRL Australia
Ottawa Canada (summer), HVAC building #4
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Ottawa Canada (summer), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Ottawa Canada (summer), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Ottawa Canada (summer), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Ottawa Canada (summer), HVAC building #1
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #2
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #3
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Ottawa Canada (summer), HVAC building #4
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)M
ean
Vot
e
ashraepmv
Karachi Pakistan (summer), NV building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33 35
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Karachi Pakistan (winter), NV building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33 35
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Multan Pakistan (summer), NV building #2
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Peshawar Pakistan (summer), NV
building #3
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Peshawar Pakistan (winter), NV building #3
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Quetta Pakistan (summer), NV building #4
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Quetta Pakistan (winter), NV building #4
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 206 MRL Australia
Saidu Sharif Pakistan (summer), NV building #5
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Saidu Sharif Pakistan (winter), NV building #5
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Karachi Pakistan (summer), NV building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33 35
ET* (degC)
Mea
n V
ote
ashraepmv
Karachi Pakistan (winter), NV building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33 35
ET* (degC)
Mea
n V
ote
ashraepmv
Multan Pakistan (summer), NV building #2
-3
-2
-1
0
1
2
3
23 25 27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Peshawar Pakistan (summer), NV building #3
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
ET* (degC)
Mea
n V
ote
ashraepmv
Peshawar Pakistan (winter), NV building #3
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
ET* (degC)
Mea
n V
ote
ashraepmv
Quetta Pakistan (summer), NV building #4
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
ET* (degC)
Mea
n V
ote
ashraepmv
Quetta Pakistan (winter), NV building #4
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
ET* (degC)M
ean
Vot
e
ashraepmv
Saidu Sharif Pakistan (summer), NV building #5
-3
-2
-1
0
1
2
3
10 15 20 25 30 35
ET* (degC)
Mea
n V
ote
ashraepmv
Saidu Sharif Pakistan (winter), NV building #5
-3
-2
-1
0
1
2
3
10 15 20 25 30 35
ET* (degC)
Mea
n V
ote
ashraepmv
Karachi Pakistan (summer), NV building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Karachi Pakistan (winter), NV
building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Multan Pakistan (summer), NV building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Peshawar Pakistan (summer), NV building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Peshawar Pakistan (winter), NV building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 207 MRL Australia
Quetta Pakistan (summer), NV building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HQuetta Pakistan (winter), NV
building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Saidu Sharif Pakistan (summer), NV building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Saidu Sharif Pakistan (winter), NV building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Karachi Pakistan (summer), NV building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
Karachi Pakistan (winter), NV building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
Multan Pakistan (summer), NV building #2
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Peshawar Pakistan (summer), NV building #3
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Peshawar Pakistan (winter), NV building #3
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)M
ean
Vot
e
ashraepmv
Quetta Pakistan (summer), NV building #4
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Quetta Pakistan (winter), NV building #4
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Saidu Sharif Pakistan (summer), NV building #5
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Saidu Sharif Pakistan (winter), NV building #5
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #1
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #2
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #3
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 208 MRL Australia
Athens Greece (summer), NV building #4
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building 5
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #6
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #1
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
ET* (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #2
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
ET* (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #3
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
ET* (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #4
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
ET* (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #5
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
ET* (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #6
-3
-2
-1
0
1
2
3
15 20 25 30 35 40
ET* (degC)M
ean
Vot
e
ashraepmv
Athens Greece (summer), NV building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Athens Greece (summer), NVbuilding #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Athens Greece (summer), NV building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Athens Greece (summer), NV
building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Athens Greece (summer), NV building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Athens Greece (summer), NV building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Athens Greece (summer), NV building #1
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 209 MRL Australia
Athens Greece (summer), NV building #2
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #3
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #4
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #5
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Athens Greece (summer), NV building #6
-3
-2
-1
0
1
2
3
10 15 20 25 30 35 40
SET (degC)
Mea
n V
ote
ashraepmv
Oxford UK (summer), NV building #1
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Oxford UK (summer), NV building #2
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Oxford UK (summer), NV building #3
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28 30
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Oxford UK (summer), NV building #1
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28
ET* (degC)M
ean
Vot
e
ashraepmv
Oxford UK (summer), NV building #2
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28
ET* (degC)
Mea
n V
ote
ashraepmv
Oxford UK (summer), NV building #3
-3
-2
-1
0
1
2
3
14 16 18 20 22 24 26 28
ET* (degC)
Mea
n V
ote
ashraepmv
Oxford UK (summer), NV building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Oxford UK (summer), NV building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Oxford UK (summer), NV building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Oxford UK (summer), NV building #1
-3
-2
-1
0
1
2
3
15 17 19 21 23 25 27 29 31 33
SET (degC)
Mea
n V
ote
ashraepmv
Oxford UK (summer), NV building #2
-3
-2
-1
0
1
2
3
15 17 19 21 23 25 27 29 31 33
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 210 MRL Australia
Oxford UK (summer), NV building #3
-3
-2
-1
0
1
2
3
15 17 19 21 23 25 27 29 31 33
SET (degC)
Mea
n V
ote
ashraepmv
Sydney Australia (summer), Mixed building #1
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Sydney Australia (summer), Mixed building #1
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27
ET* (degC)
Mea
n V
ote
ashraepmv
Sydney Australia (summer), Mixed building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Sydney Australia (summer), Mixed building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
Sydney Australia (winter), Mixed building #1
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Sydney Australia (winter), Mixed building #1
-3
-2
-1
0
1
2
3
17 19 21 23 25 27
ET* (degC)
Mea
n V
ote
ashraepmv
Sydney Australia (winter), Mixed building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Sydney Australia (winter), Mixed building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
SET (degC)M
ean
Vot
e
ashraepmv
Sydney Australia (winter), HVAC building #2
-3
-2
-1
0
1
2
3
21 22 23 24
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Sydney Australia (winter), HVAC building #2
-3
-2
-1
0
1
2
3
20.5 21.5 22.5 23.5
ET* (degC)
Mea
n V
ote
ashraepmv
Sydney Australia (winter), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Sydney Australia (winter), HVAC
building #2
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer) HVAC building #2
-3
-2
-1
0
1
2
3
20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #3
-3
-2
-1
0
1
2
3
20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #4
-3
-2
-1
0
1
2
3
20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 211 MRL Australia
San Francisco Bay Area RP-462 (summer), HVAC building #7
-3
-2
-1
0
1
2
3
20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #8
-3
-2
-1
0
1
2
3
20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #9
-3
-2
-1
0
1
2
3
20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #10
-3
-2
-1
0
1
2
3
20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #2
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #3
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #4
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #7
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #8
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)M
ean
Vot
e
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #9
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #10
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (summer), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (summer), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (summer), HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (summer), HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 212 MRL Australia
San Francisco Bay Area RP-462 (summer), HVAC building #9
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HSan Francisco Bay Area RP-462 (summer), HVAC building #10
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (summer), HVAC building #2
-3
-2
-1
0
1
2
3
18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #3
-3
-2
-1
0
1
2
3
18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #4
-3
-2
-1
0
1
2
3
18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #7
-3
-2
-1
0
1
2
3
18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #8
-3
-2
-1
0
1
2
3
18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #9
-3
-2
-1
0
1
2
3
18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), HVAC building #10
-3
-2
-1
0
1
2
3
18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5
SET (degC)M
ean
Vot
e
ashraepmv
San Francisco Bay Area RP-462 (summer), NV building #1
-3
-2
-1
0
1
2
3
20.5 22.5 24.5 26.5 28.5 30.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), NV building #5
-3
-2
-1
0
1
2
3
20.5 22.5 24.5 26.5 28.5 30.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), NV building #6
-3
-2
-1
0
1
2
3
20.5 22.5 24.5 26.5 28.5 30.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462
(summer), NV building #1
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), NV building #5
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), NV building #6
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), NV building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 213 MRL Australia
San Francisco Bay Area RP-462 (summer), NV building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HSan Francisco Bay Area RP-462
(summer), NV building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (summer), NV building #1
-3
-2
-1
0
1
2
3
20.5 22.5 24.5 26.5 28.5 30.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), NV building #5
-3
-2
-1
0
1
2
3
20.5 22.5 24.5 26.5 28.5 30.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (summer), NV building #6
-3
-2
-1
0
1
2
3
20.5 22.5 24.5 26.5 28.5 30.5
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Area RP-462 (winter), HVAC building #2
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Area RP-462 (winter), HVAC building #3
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Area RP-462 (winter), HVAC building #4
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Area RP-462 (winter), HVAC building #7
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
Operative Temp (degC)M
ean
Vot
e
ashraepmv
San Francisco Area RP-462 (winter), HVAC building #8
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Area RP-462 (winter), HVAC building #9
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Area RP-462 (winter), HVAC building #10
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter),
HVAC building #2
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #3
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #4
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #7
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 214 MRL Australia
San Francisco Bay Area RP-462 (winter), HVAC building #8
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #9
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #10
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Area RP-462 (winter), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Area RP-462 (winter), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Area RP-462 (winter), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Area RP-462 (winter), HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Area RP-462 (winter), HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Area RP-462 (winter), HVAC building #9
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
San Francisco Area RP-462 (winter), HVAC building #10
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (winter), HVAC building #2
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #3
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter),
HVAC building #4
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #7
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #8
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #9
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 215 MRL Australia
San Francisco Bay Area RP-462 (winter), NV building #1
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5 25.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), NV building #5
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5 25.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), NV building #6
-3
-2
-1
0
1
2
3
19.5 20.5 21.5 22.5 23.5 24.5 25.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), NV building #1
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), NV building #5
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), NV building #6
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5
ET* (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), NV building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (winter), NV building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
San Francisco Bay Area RP-462 (winter), NV building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Francisco Bay Area RP-462 (winter), NV building #1
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28 29
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), NV building #5
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28 29
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), HVAC building #10
-3
-2
-1
0
1
2
3
18 20 22 24 26 28 30
SET (degC)
Mea
n V
ote
ashraepmv
San Francisco Bay Area RP-462 (winter), NV building #6
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26 27 28 29
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #1
-3
-2
-1
0
1
2
3
20 22 24 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #2
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #3
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 216 MRL Australia
Townsville Australia RP-702 (summer-dry), HVAC building #4
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #5
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #6
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #7
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #8
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #9
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #10
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #11
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #12
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
Operative Temperature (degC)M
ean
Vot
e
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #1
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #2
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #3
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884
(summer-dry), HVAC building #4
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #5
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #6
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #7
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 217 MRL Australia
Townsville Australia RP-884 (summer-dry), HVAC building #8
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #9
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #10
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #11
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-884 (summer-dry), HVAC building #12
-3
-2
-1
0
1
2
3
20 21 22 23 24 25 26
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
Townsville Australia RP-702 (summer-dry), HVAC building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #9
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #10
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-dry), HVAC building #11
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 218 MRL Australia
Townsville Australia RP-702 (summer-dry), HVAC building #12
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HTownsville Australia RP-702
(summer-dry), HVAC building #1
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #2
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #3
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #4
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #5
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #6
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #7
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #8
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)M
ean
Vot
e
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #9
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #10
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-dry), HVAC building #11
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702
(summer-dry), HVAC building #12
-3
-2
-1
0
1
2
3
20 22 24 26 28 30 32
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #1
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #3
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #4
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 219 MRL Australia
Townsville Australia RP-702 (summer-wet), HVAC building #5
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #6
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #7
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #8
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #9
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #10
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #11
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #12
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #1
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)M
ean
Vot
e
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #3
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #4
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #5
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702
(summer-wet), HVAC building #6
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #7
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #8
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #9
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 220 MRL Australia
Townsville Australia RP-702 (summer-wet), HVAC building #10
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #11
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #12
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28
ET* (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMVA
SH
Townsville Australia RP-702 (summer-wet), HVAC building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #9
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #10
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #11
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #12
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Townsville Australia RP-702 (summer-wet), HVAC building #1
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #3
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 221 MRL Australia
Townsville Australia RP-702 (summer-wet), HVAC building #4
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #5
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #6
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #7
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #8
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #9
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #10
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #11
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)
Mea
n V
ote
ashraepmv
Townsville Australia RP-702 (summer-wet), HVAC building #12
-3
-2
-1
0
1
2
3
21 22 23 24 25 26 27 28 29 30
SET (degC)M
ean
Vot
e
ashraepmv
Merseyside UK (summer), NV building #1
-3
-2
-1
0
1
2
3
16 17 18 19 20 21 22 23 24 25 26
Operative Temp (degC)
Mea
n V
ote
pmv
Merseyside UK (summer), NV building #2
-3
-2
-1
0
1
2
3
16 17 18 19 20 21 22 23 24 25 26
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (summer), NV building #3
-3
-2
-1
0
1
2
3
16 17 18 19 20 21 22 23 24 25 26
Operative Temp (degC)
Mea
n V
ote
ashraepmv
no ash versus pmv graph for building 1, Merseyside UK.
Merseyside UK (summer), NV building #1
-3
-2
-1
0
1
2
3
16 18 20 22 24 26
ET* (degC)
Mea
n V
ote
pmv
Merseyside UK (summer), NV building #2
-3
-2
-1
0
1
2
3
16 18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (summer), NV building #3
-3
-2
-1
0
1
2
3
16 18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 222 MRL Australia
Merseyside UK (summer), NV building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HMerseyside UK (summer), NV
building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Merseyside UK (summer), NV building #1
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26 27
SET (degC)
Mea
n V
ote
pmv
Merseyside UK (summer), NV building #2
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26 27
SET (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (summer), NV building #3
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26 27
SET (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #3
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #5
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #6
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #7
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26
Operative Temp (degC)M
ean
Vot
e
ashraepmv
Merseyside UK (winter), NV building #8
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #3
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #5
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV
building #6
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #7
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #8
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #3
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 223 MRL Australia
Merseyside UK (winter), NV building #5
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
HMerseyside UK (winter), NV
building #6
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Merseyside UK (winter), NV building #7
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Merseyside UK (winter), NV building #8
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Merseyside UK (winter), NV building #3
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26 27
SET (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #5
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26 27
SET (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #6
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26 27
SET (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #7
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26 27
SET (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), NV building #8
-3
-2
-1
0
1
2
3
18 19 20 21 22 23 24 25 26 27
SET (degC)M
ean
Vot
e
ashraepmv
Merseyside UK (winter), Mixed building #4
-3
-2
-1
0
1
2
3
18.5 20.5 22.5 24.5 26.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), Mixed building #4
-3
-2
-1
0
1
2
3
18 20 22 24 26
ET* (degC)
Mea
n V
ote
ashraepmv
Merseyside UK (winter), Mixed building #4
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Merseyside UK (winter), Mixed building 4
-3
-2
-1
0
1
2
3
19 20 21 22 23 24 25 26 27
SET (degC)
Mea
n V
ote
ashraepmv
Singapore (summer), HVAC building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33 35
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Singapore (summer), HVAC building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31 33 35
ET* (degC)
Mea
n V
ote
ashraepmv
Singapore (summer), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
ASHRAE RP-884 Final Report
Appendix A page 224 MRL Australia
Singapore (summer), HVAC building #1
-3
-2
-1
0
1
2
3
17 19 21 23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
Singapore (summer), NV building #2
-3
-2
-1
0
1
2
3
26 27 28 29 30 31 32
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Singapore (summer), NV building #2
-3
-2
-1
0
1
2
3
27 29 31 33 35 37
ET* (degC)
Mea
n V
ote
ashraepmv
Singapore (summer), NV building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Singapore (summer), NV building #2
-3
-2
-1
0
1
2
3
23 25 27 29 31 33 35 37
SET (degC)
Mea
n V
ote
ashraepmv
Grand Rapids Michigan US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
22.5 23.5 24.5
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Grand Rapids Michigan US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
22 23 24
ET* (degC)
Mea
n V
ote
ashraepmv
Grand Rapids Michigan US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Grand Rapids Michigan US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
23 24 25 26 27 28
SET (degC)M
ean
Vot
e
ashraepmv
San Ramon CA US (summer), HVAC building #3
-3
-2
-1
0
1
2
3
21 22 23 24
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Ramon CA US (summer), HVAC building #3
-3
-2
-1
0
1
2
3
21 22 23 24
ET* (degC)
Mea
n V
ote
ashraepmv
San Ramon CA US (summer), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Ramon CA US (summer), HVAC building #3
-3
-2
-1
0
1
2
3
21.5 22.5 23.5 24.5 25.5 26.5 27.5
SET (degC)
Mea
n V
ote
ashraepmv
San Ramon CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
19 20 21 22 23 24
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Ramon CA US (winter), HVAC building #2
-3
-2
-1
0
1
2
3
19 20 21 22 23 24
Operative Temp (degC)
Mea
n V
ote
ashraepmv
San Ramon CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
19 20 21 22 23 24
ET* (degC)
Mea
n V
ote
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 225 MRL Australia
San Ramon US (winter), HVAC building #2
-3
-2
-1
0
1
2
3
19 20 21 22 23 24
ET* (degC)
Mea
n V
ote
ashraepmv
San Ramon CA US (winter), HVACbuilding #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Ramon CA US (winter), HVAC building #2
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
San Ramon CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5 27.5
SET (degC)
Mea
n V
ote
ashraepmv
San Ramon CA US (winter), HVAC building #2
-3
-2
-1
0
1
2
3
19.5 21.5 23.5 25.5 27.5
SET (degC)
Mea
n V
ote
ashraepmv
Auburn CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
20 21 22 23 24
Operative Temp (degC)
Mea
n V
ote
ashraepmv
Auburn CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
20 21 22 23 24
ET* (degC)
Mea
n V
ote
ashraepmv
Auburn CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
-3 -2 -1 0 1 2 3
PMV
AS
H
Auburn CA US (winter), HVAC building #1
-3
-2
-1
0
1
2
3
19 21 23 25 27 29 31
SET (degC)M
ean
Vot
e
ashraepmv
ASHRAE RP-884 Final Report
Appendix A page 226 MRL Australia
ASHRAE RP-884 Final Report
Appendix B page 227 MRL Australia
APPENDIX B - PREFERRED TEMPERATURE FOR EACH BUILDING IN THE RP-884 DATABASE
ASHRAE RP-884 Final Report
Appendix B page 228 MRL Australia
San Francisco Bay Area RP-462 (summer), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (summer), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (summer), HVAC building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (summer), HVAC building #7
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (summer), HVAC building #8
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (summer), HVAC building #9
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (summer), NV building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (summer), NV building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (summer), NV building #6
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)San Francisco Bay Area RP-462 (winter),
NV building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (winter), NV building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (winter), NV building #6
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (winter),
HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (winter), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462(winter), HVAC building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (winter), HVAC building #7
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462 (winter), HVAC building #8
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Francisco Bay Area RP-462(winter), HVAC building #9
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
ASHRAE RP-884 Final Report
Appendix B page 229 MRL Australia
San Francisco Bay Area RP-462 (winter), HVAC building #10
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Antioch CA US (winter), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Auburn CA US (winter), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Bangkok Thailand (summer), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Bangkok Thailand (summer), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Bangkok Thailand (summer), NV building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Bangkok Thailand (summer), NV building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)Brisbane Australia (summer),
HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer), HVAC building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer), HVAC building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer),
NV building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer), NV building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer), NV building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer), NV building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Brisbane Australia (summer), NV building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-dry), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
ASHRAE RP-884 Final Report
Appendix B page 230 MRL Australia
Darwin Australia (summer-dry), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-dry), HVAC building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-dry), HVAC building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-dry), HVAC building #6
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-dry), HVAC building #7
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-dry), HVAC building #8
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-wet), HVAC building #8
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-wet), HVAC building #9
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-wet), HVAC building #11
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)Darwin Australia (summer-wet),
HVAC building #12
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-wet), HVAC building #13
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Darwin Australia (summer-wet), HVAC building #14
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Melbourne Australia (summer),
HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Melbourne Australia (summer), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Melbourne Australia (summer), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Melbourne Australia (summer), HVAC building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Melbourne Australia (summer), NV building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Melbourne Australia (summer), NV building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
ASHRAE RP-884 Final Report
Appendix B page 231 MRL Australia
Townsville Australia RP-702 (summer-dry), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-dry), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-dry), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-dry), HVAC building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-dry), HVAC building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-dry), HVAC building #7
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-dry), HVAC building #8
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-dry), HVAC building #10
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-dry), HVAC building #11
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)Townsville Australia RP-702
(summer-dry), HVAC building #12
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-wet), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-wet), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702
(summer-wet), HVAC building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-wet), HVAC building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-wet), HVAC building #6
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-wet), HVAC building #7
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-wet), HVAC building #8
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-884 (summer-wet), HVAC building #9
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
ASHRAE RP-884 Final Report
Appendix B page 232 MRL Australia
Townsville Australia RP-702 (summer-wet), HVAC building #10
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Townsville Australia RP-702 (summer-wet), HVAC building #12
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Karachi Pakistan (winter), NV building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Multan Pakistan (summer), NV building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Peshawar Pakistan (summer), NV building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Peshawar Pakistan (winter), NV building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Quetta Pakistan (summer), NV building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Quetta Pakistan (winter), NV building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Saidu Sharif Pakistan (summer), NV building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
) Athens Greece (summer), NV
building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Athens Greece (summer), NV building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Athens Greece (summer), NV building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Athens Greece (summer), NV
building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Athens Greece (summer), NV building #6
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Oxford UK (summer), NV building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Oxford UK (summer), NV building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Oxford UK (summer), NV building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Ramon CA US (summer), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
ASHRAE RP-884 Final Report
Appendix B page 233 MRL Australia
San Ramon CA US (winter), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
San Ramon CA US (winter), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Grand Rapids Michigan (winter), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (summer), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (summer), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (summer), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (summer), HVAC building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (summer), HVAC building #6
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (summer), HVAC building #7
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)Montreal Canada RP-821
(summer), HVAC building #9
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (summer), HVAC building #10
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (summer), HVAC building #11
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821
(summer), HVAC building #12
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (winter), HVAC building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (winter), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (winter), HVAC building #3
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (winter), HVAC building #5
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (winter), HVAC building #6
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
ASHRAE RP-884 Final Report
Appendix B page 234 MRL Australia
Montreal Canada RP-821 (winter), HVAC building #7
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (winter), HVAC building #8
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (winter), HVAC building #9
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Montreal Canada RP-821 (winter), HVAC building #10
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
South Wales UK (summer), HVAC building #4
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Sydney Australia (summer), Mixed building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Sydney Australia (winter), Mixed building #1
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
Sydney Australia (winter), HVAC building #2
0
20
40
60
80
100
16 18 20 22 24 26 28 30 32 34 36
indoor operative temperature (oC)
perc
enta
ge w
antin
g ch
ange
(%
)
ASHRAE RP-884 Final Report
Appendix C page 235 MRL Australia
APPENDIX C - SUMMARY OF THE ORIGINAL FIELD EXPERIMENTS COMPRISING THE ASHRAE RP-884 DATABASE
ASHRAE RP-884 Final Report
Appendix C page 236 MRL Australia
C.1. Project Title - ASHRAE TC 2.1 sponsored RP-702
Project filenames in the RP-884 database
This project is disseminated as file numbers 36 (summer “dry” - HVAC) and 37 (summer
“wet” - HVAC) in the RP-884 database.
Project researchers and class of investigation
Richard de Dear (Macquarie University, Sydney Australia) and Marc Fountain (University of
California at Berkeley, USA).
A CLASS-1 field experiment sponsored by ASHRAE TC 2.1.
Project publications
de Dear, R.J. and M.E. Fountain (1994) "Field experiments on occupant comfort and office
thermal environments in a hot-humid climate," ASHRAE Transactions, Vol.100(2), pp.457-
475.
de Dear, R.J. and M.E. Fountain (1994) Cover feature -- "Thermal comfort in air-conditioned
office buildings in the tropics," Journal of the Australian Institute of Refrigerating, Air-
Conditioning and Heating, Vol.48(9), pp.14-30.
de Dear, R.J., M.E. Fountain, S. Popovic, S. Watkins, G. Brager, E.Arens and C Benton
(1993) A Field Study of Occupant Comfort and Office Thermal Environments in a Hot-Humid
Climate : Final Report on ASHRAE RP-702. (MRL: Sydney), 162 pp.
Project location, climate and season
The project was located in Townsville on the north-eastern coast of Australia which falls
within a Tropical Savanna climate zone (wet-dry tropics). One field experiment conducted in
the “Dry” season (warm-dry “summer”), another experiment conducted in the “wet” season
(hot-wet “summer”).
ASHRAE RP-884 Final Report
Appendix C page 237 MRL Australia
Sample buildings
Twelve buildings, all offices, were studied.
Building Code
(blcode)
Sample Size (n)
Climate Controls
(bldgtype)
Floor Area
Occupancy Pattern
1 56 VAV 2,010m2 3 storeys, mainly open plan.
Federal Government department.
2 22 Central AC (CAV)
3,944 m2 4 levels, private and mult-occupant.offices.
Tertiary education administration.
3 61 VAV 17,820 m2 12 storeys mainly open plan.
multi-tenant office tower.
4 22 CAV 4,865 m2 Twin tower design, mainly open plan.
Government departments
5 45 CAV 1,860 m2 5 storeys, mainly open plan.
Single tenant - power utility administration.
6 38 CAV 4,632 m2 8 storeys, mainly open plan.
Regional bank headquarters.
7 100 CAV 4,851 m2 3 storeys, mainly open plan.
Local government offices.
8 105 CAV 7,780 m2 5 storeys, mainly open plan.
State government offices.
9 14 CAV 1,727 m2 3 storeys, mainly open plan.
State government offices.
10 63 VAV 2,076 m2 3 storeys, mainly open plan.
Office building.
11 19 VAV 3,942 m2 6 storeys, mainly open plan.
Insurance and legal firms.
12 82 VAV 22,910 m2 13 storeys, mainly open plan.
Government department.
Instruments
Class-1 instrumentation includes three heights above floor level. Anemometry was
measured by DANTEC 54R10 omnidirectional heated elements with fast time-constant for
turbulence intensity calculations. Air temperature was measured by YSI series 700 probes
(thermistors) and globe temperatures measured by fixing a table tennis ball (40mm diam.)
over the sensor with appropriate steps taken to achieve correct emissivity. Dewpoint
temperature (humidity) measured by a General Eastern DEW-10 chilled-mirror transducer.
ASHRAE RP-884 Final Report
Appendix C page 238 MRL Australia
Radiant asymmetry was measured by a Bruel and Kjaer plane radiant asymmetry sensor
(MM 0036).
Questionnaire
The questionnaire was divided into two parts, background and on-line surveys. The
background questionnaire covered demographics, contextual and psychological factors. The
on-line questionnaire covered the subjects assessment of their immediate thermal
environment, such as their thermal sensation on a 7-point scale, acceptability as a yes/no
response, thermal preference on a 3-point scale, current garment insulation assessed by
tables and algorithms in ASHRAE Standard 55-1992 and metabolic activity assessed by
ASHRAE Standard 55-92 and ISO 7730. Metabolic activity was recorded at four distinct
time periods, from which an overall metabolic rate was established. The on-line
questionnaire was conducted at the same time as physical measurements were being made
of the subjects environment.
Outdoor meteorological data
Concurrent three-hourly observations from Townsville Airport (purchased from Australian
Bureau of Meteorology), from which air temperature and relative humidity at 600 hours and
1500 hours was extracted for RP-884 purposes.
RP-884 standardization assumptions
The design of the database structure and coding conventions throughout the ASHRAE
Adaptive Model Project (RP-884) was based on de Dear and Fountains’ (1994) Townsville
(RP-702) project.
ASHRAE RP-884 Final Report
Appendix C page 239 MRL Australia
C.2. Project Title - Thermal comfort studies in modern industrial buildings.
Project file names in the RP-884 database
This project is disseminated as file numbers 1 (summer - HVAC) and 2 (winter - HVAC) in
the RP-884 database.
Project researchers and class of investigation
Jill C. Brown (Ph.D thesis, University of Wales, Cardiff). This is a CLASS-2 field
experiment.
Project publications
Brown, J. C. (1995). Thermal Comfort Studies in Modern Industrial Buildings, Ph.D. Thesis,
University of Wales, Cardiff.
Brown, J. C. and Jones, P. J. (1993). Thermal Comfort in Modern Industrial Buildings, Clima
2000 Conference, London, Organised by the Chartered Institute of Building Services
Engineers.
Project location, climate and season
This project was conducted in South Wales, UK. More precisely, in Cwmbran, Gwent;
Newport, Gwent; Ebbw Vale, Gwent; Maesteg, Mid Glamorgan; Cwmfelinfach, Gwent;
Gwent; Llanelli, Dyfed and Llantrisant, Glamorgan. Summer and Winter seasons
investigated. Climatically, this region can be classified as west coast marine.
Instruments
Indoor climatic instrumentation included: pre-calibrated thermistors to measure air
temperature, hot-wire anemometer for air speed, solid-state hygrometer to measure
humidity, and a thermistor inside a 38mm diameter ping-pong ball to measure globe
temperature. Air temperature was measured at ankle, waist and head heights (0.3m, 1.5m
and 2m) while all other parameters were only measured at waist height.
Questionnaire
The questionnaire addressed both conditions at the time of physical measurements and
typical/overall conditions, of which only the former was used for RP-884’s purposes.
ASHRAE RP-884 Final Report
Appendix C page 240 MRL Australia
Sensation was rated on the ASHRAE 7-pt scale. The questionnaire assessed thermal
preference but not thermal acceptability. Metabolic ratings were established at the time of
the questionnaire and prior to questionnaire, using the ASHRAE 55-92 standard for
guidance. However, the author expressed reservations that this checklist did not fully
describe the types of activities being performed within the study. Clo was estimated using
the ASHRAE 55-92 and ISO/DIS 9920-91 checklist and if clothing insulation data was
absent then an estimation was made using the garment weight relationship suggested by
McCullough et al., 1984).
Sample buildings
Location Building Code
(blcode)
Sample Size (n)
and season
Climate Controls
(bldgtype)
Floor Area
Occupancy Pattern
Cwmbran, Gwent
1 16 - winter HVAC 576m2 Light Industrial Factory
Newport, Gwent 2 17 - summer HVAC 3000m2 Med-heavy Industrial Factory
Ebbw Vale, Gwent
3 15 - summer HVAC 1000m2 Light-med Industrial Factory
Maesteg, Glamorgan
4 32 - summer HVAC c. 1500m2
Light Industrial Factory
Llanelli, Dyfed 5 6 - winter HVAC c. 850m2
Light Industrial Factory
Llantrisant, mid - Glamorgan
6 9 - winter HVAC c. 2500m2
Light Industrial Factory
Llantrisant, mid - Glamorgan
7 7 - winter HVAC c. 2500m2
Med-heavy Industrial Factory
Cwmfelinfach, Gwent
8 16 - summer HVAC c. 6700m2
Med-heavy Industrial Factory
Outdoor meteorological data
In the absence of accessible outdoor meteorological observations at the same time as the
questionnaire data, RP-884 researchers substituted climatological data at 600 hrs and 1500
hrs. This data was retrieved from two sources -- air temperature from the journal Weather
(using the UK Met Office site of Roose), and humidity (by derivation of dew point using the
ASHRAE RP-884 Final Report
Appendix C page 241 MRL Australia
UK Met Office site for Cardiff) based on data entries the International Station Meteorological
and Climate Summary (ISMCS 1992) CDROM.
RP-884 standardization assumptions
Instrumentation in the original data set took measurements at heights 2m, 1.5m and 0.3m.
We mapped 0.3m to 0.1m and 1.5m to 1.1m for the RP-884 database. Clo was estimated
with the ASHRAE 55-92 checklist so no corrections were needed, but the activity variable in
the original data set had to be used to determine whether or not the subject was seated and
so whether 0.15 clo for the insulation due to a chair needed to be subtracted. This provided
two variables within the RP-884 database, clothing insulation with and without the effects of
a chair. The research design was cross-sectional which satisfied the assumptions for RP-
884, that all subjects were independent.
ASHRAE RP-884 Final Report
Appendix C page 242 MRL Australia
C.3. Project Title - Doctoral dissertation. From comfort to kilowatts: an integrated
assessment of electricity conservation in Thailand’s commercial sector.
Project file names in the RP-884 database
This project is disseminated as file numbers 3 (summer - HVAC) and 4 (summer - NV) in the
RP-884 database.
Project researchers and class of investigation
John F. Busch, Jr (Lawrence Berkeley Lab. Berkeley California, USA).
This is a CLASS-2 field experiment.
Project publications
Busch, 1990 “Thermal responses to the Thai office environment.” ASHRAE Trans., V. 96(1),
pp. 859-872.
Busch J. F. (1992) A tale of two populations: thermal comfort in air-conditioned and naturally
ventilated offices in Thailand. Energy and Buildings Vol 18 pp 235-249.
Busch J. (1995) Thermal comfort in Thai air-conditioned and naturally ventilated offices in
Thailand Standards for thermal comfort pp 114-121.
Busch J. F. (1990) From Comfort to Kilowatts - An Integrated Assessment of Electricity
Conservation in Thailand’s Commercial Sector. (UC Berkeley PhD. Thesis).
Project location, climate and season
The project was located in Bangkok, Thailand (peninsular, Southeast Asia). Bangkok is the
largest city in Thailand as well as being the capital. Being tropical, Bangkok does not display
much seasonality and can been classified under a hot humid climate. The project was
conducted in the hot season and the wet season.
ASHRAE RP-884 Final Report
Appendix C page 243 MRL Australia
Sample buildings
Building Code (blcode)
Sample Size (n)
Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 380 HVAC Sathorn Thani (SH and SW)
2 389 HVAC Thai Farmers Bank (TH and TW)
3 194 NV Dept. Science Services (DH and DW)
4 173 NV Ministry of ST and E (MH and MW)
5 25 NV KMIT Instit. Tech (pilot) (PH and PW)
Instruments
The instrumentation was packaged into a “toolbox” which was placed in the subject’s
occupied zone, typically at a height of 0.6m above floor, or on their desk. Air and globe
temperatures were registered with calibrated thermistors. The globe thermometer was
based on a 38mm ping pong ball. Air speeds were registered with a Kurz 403 “hot-film”
anemometer in the vicinity of the subject. Humidity was recorded with a steady-state device.
All sensors were connected to a Campbell Scientific CR21 datalogger which was dumped
into a tape recorder at the end of every day in the field.
Questionnaire
Subjects who had been seated at their workstations for more than 15 minutes were eligible
for inclusion in the sample. The questionnaire covered basic sensation and preference
items. Metabolic and clothing scales/check-lists were based on the McIntyre (1980) tables.
Outdoor meteorological data
Outdoor meteorological data were collected by the original researcher from the Royal Thai
Meteorological Department. Daily maxima and minima for temperature and humidity were
extracted for the RP-884 database.
RP-884 standardization assumptions
For this study clo was estimated by the McIntrye (1980) method. Clo therefore required
correction to the ASHRAE 55-92 Standard for RP-884 purposes. To this 0.15 clo was
added to create a separate variable accounting for the clothing ensemble and insulation
ASHRAE RP-884 Final Report
Appendix C page 244 MRL Australia
effects of a chair. The research design of this project was cross-sectional which satisfied the
assumptions for RP-884, that all subjects were independent.
ASHRAE RP-884 Final Report
Appendix C page 245 MRL Australia
C.4. Project Title - The CSAA, Antioch (1995) component of the Advanced Customer
Technology Test (ACT2) project.
Project researchers and class of investigation
Charles C. Benton and Gail S. Brager (CEDR, Department of Architecture, University of
California, Berkeley). This is a CLASS-1 field experiment.
Project file names in the RP-884 database
This project is disseminated as file number 5 in the RP-884 database.
Project publications
Benton, C. et al. Advanced Customer Technology Test (ACT2) CSAA Progress Report.
(CEDR UC Berkeley).
Brager, G et al. (1994) “A comparison of methods for assessing thermal sensation and
acceptability in the field,” In Thermal Comfort: Past, Present and Future. (eds N. A. Oseland
and M. A. Humphreys).
Project location, climate and season
The ACT2 project was based on the ASHRAE RP-702 project (the hot-humid field
experiment in Townsville Australia, de Dear et al., 1994). Data was collected for the ACT2
project between 1991 and 1995 at four sites. The Sunset Building (baseline and post-
retrofit) in San Ramon, Verifone (baseline) in Auburn and CSAA (post construction) in
Antioch. Antioch has a Mediterranean climate, less than 50 km inland from the San
Francisco Bay but separated from the water by the Berkeley Hills (nearest major city is
Concord). The season of this study was winter.
Sample buildings
Building Code (blcode)
Sample Size (n)
Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 111 HVAC office building
Instruments
A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m. The
sensors chosen were selected to meet the response time and accuracy requirements of
ASHRAE RP-884 Final Report
Appendix C page 246 MRL Australia
ASHRAE Standard 55-81 and ISO Standard 7730 for thermal assessment. YSI series 700
probes with vinyl-coated tips were used to measure air temperature. Globe temperature was
measured by attaching a 38 mm diameter table tennis ball on the temperature sensors. The
balls were painted grey for correct emissivity. Air velocity was measured by Dantec 54R10
anemometers, which are omnidirectional fully temperature-compensated sensors. Dewpoint
temperature was measured by a General Eastern DEW-10 chilled mirror dewpoint
transducer. All parameters were measured at all three heights except dewpoint temperature
which was only measured at 0.6m. Radiant asymmetry and illuminance were also
measured, but are not essential to the purpose of RP-884.
Questionnaire
The questionnaire consisted of an on-line questionnaire, which addressed conditions at the
time physical measurements were being taken and a background questionnaire. The latter
covered subject details such as, health and emotional characteristics, office description,
work area and job satisfaction, environmental sensitivity, plus personal comfort, satisfaction
and perceived control. In the on-line section thermal sensation was rated on the 7-pt
ASHRAE scale. Thermal preference was assessed on a descriptive 3-pt scale. Thermal
acceptability was not rated. Metabolic rate was estimated based on a checklist referring to
the subjects activity in the 15 minutes before completing the on-line questionnaire, using
tables in the ASHRAE Handbook of Fundamentals (HOF, 1985). Clo estimates were based
on responses to the clothing item checklist provided in the on-line questionnaire from the
ASHRAE Standard 55-81 method.
Outdoor meteorological data
Meteorological air temperature data at 600 hrs and 1500 hrs were purchased by RP-884
from the National Oceanic and Atmospheric Administration’s National Climatic Data Center.
Relative humidity, also at 600 hrs and 1500 hrs was extracted from the International Station
Meteorological and Climate Summary CD-ROM (ISMCS, 1992) for the nearest site.
RP-884 standardization assumptions
The detailed methods and protocol used in ASHRAE RP-462 (and extended to the
ASHRAE RP-702 project described above) were carried out in full for the ACT2 Project.
Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462, little
ASHRAE RP-884 Final Report
Appendix C page 247 MRL Australia
standardisation was necessary. However, clothing was based on the ASHRAE 55-81
method, and so required conversion into equivalent ASHRAE 55-92 values. 0.15 clo was
then added for chair insulation. The research design of this field experiment was longitudinal,
so for the purposes of RP-884, independence between subjects was assumed.
ASHRAE RP-884 Final Report
Appendix C page 248 MRL Australia
C.5. Project Title - Higher PMV causes higher energy consumption in air-
conditioned buildings: A case study in Jakarta, Indonesia.
Project researchers and class of investigation
Tri H. Karyono (University of Sheffield, UK). This is a CLASS-3 field experiment.
Project file names in the RP-884 database
This project is disseminated as file numbers 6 (summer - HVAC bdgs), 7 (summer - NV)
and 8 (summer - mixed mode buildings) in the RP-884 database.
Project publications
Karyono, T. H. (1995) “Higher PMV causes higher energy consumption in air-conditioned
buildings: A case study in Jakarta, Indonesia, “ Standards for thermal comfort. ed by Fergus
Nicol, Michael Humphreys, Oliver Sykes and Susan Roaf. Chapman and Hall pp 219-226.
Karyono, T. (1996) “Thermal comfort in the tropical southeast Asia region.” Architectural
Science Review. V39(3), pp.135-139.
Karyono, T.H (1996) “Discrepancy between actual and predicted thermal votes of
Indonesian workers in Jakarta, Indonesia.” International Journal of Ambient Energy.
V.17(2), pp.95-100.
Project location, climate and season
The project was located in Jakarta, Indonesia. This is in a wet equatorial climate zone with a
season classified as “summer” all year round.
Sample buildings
Building Code (blcode)
Sample Size (n)
Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 97 NV office building 2 103 HVAC office building 3 98 HVAC office building 4 96 HVAC office building 5 91 HVAC office building 6 41 Mixed (hybrid) office building 7 70 HVAC office building
ASHRAE RP-884 Final Report
Appendix C page 249 MRL Australia
Instruments
Bruel and Kjaer 1212 Thermal Comfort Meter. No anemometer used in this project.
Relative humidity were measured with a solid state hygrometer.
Questionnaire
In Bahasa Indonesian.
Outdoor meteorological data
Outdoor climatological air temperature and relative humidity data at 600 hrs and 1500 hrs
were obtained from the International Station Meteorological and Climate Summary (ISMCS,
1992) CD-ROM for Jakarta.
RP-884 standardization assumptions
The B+K 1212 instrument was used to measure operative and equivalent temperatures. As
a result no radiant temperatures could be calculated (from globe temperature). Clothing
estimates were based on the Bruel and Kjaer manual which closely corresponds to the ISO
7730 (1984) methods and which was mapped to the ASHRAE 55-92 standard for RP-884.
Chair insulation estimates of 0.15 clo were also added to form a total insulation variable in
the RP-884 database. The research design was cross-sectional which satisfied the
assumptions for RP-884, that all subjects were independent.
ASHRAE RP-884 Final Report
Appendix C page 250 MRL Australia
C.6. Project Title - Montreal ASHRAE RP-821.
“Field Study of Occupant Comfort and Office Thermal Environments in a Cold Climate.”
This is the third of a series of ASHRAE projects (following RP-462 in San Francisco and
RP-702 in a hot-humid climate).
Project researchers and class of investigation
Giovanna Donnini, Jean Molina, Carlo Martello, Dorothy Ho Ching Lai, Kit Ho Lai, Ching Yu
Chang, Michel Laflamme, Van Hiep Nguyen, Fariborz Haghighat (Auger, Donnini and
Nguyen Inc.). This is a CLASS 1 field experiment in line with the preceding two ASHRAE-
sponsored field experiments.
Project file names in the RP-884 database
This project is disseminated as file numbers 9 (summer - HVAC) and 10 (winter - HVAC) in
the RP-884 database.
Project publications
Donnini, G. et al (1996) Field Study of Occupant Comfort and Office Thermal Environments
in a Cold Climate: Final Report. ADN Inc., Montreal, Quebec, Canada.
Project location, climate and season
The cities chosen for the study are Montreal, Longueuil, Gramby, Cap-de-la-Madeleine,
Shawinigan, Trois-Rivieres, Hull and Maniwaki in Canada. They are all located along the
border of the Northern and Southeastern limits. The climatic classification is towards the
cold extreme of the humid mid latitudes. Data were collected in both summer and winter
seasons.
Instruments
Air temperatures were measured using Dantec 54R10 thermistors. Globe temperatures
were measured using Bruel and Kjaer globe temperature sensors (MM 0030) each
consisting of a Pt100 (platinum resistance) temperature sensing element situated in the
centre of a 150mm diameter globe of appropriate emissivity. Air velocity and turbulence
were measured by Dantec 54R10 anemometers, which are omnidirectional fully
temperature-compensated sensors. The factory calibrated the sensors the week preceding
ASHRAE RP-884 Final Report
Appendix C page 251 MRL Australia
the start of the site visits. Dew point temperature and vapour pressure was measured by a
Bruel and Kjaer air humidity transducer (MM 0037). Air temperature, globe temperature, air
velocity and turbulence were measured at three heights (ankle, waist and head height) and
dew point temperature was measured only at waist height.
Sample Buildings
Building Code and Location
Sample Size (n)
Climate Controls
(bldgtype)
Floor Area
Occupancy Pattern
1 Montreal
summer 39 winter (37)
free cooling, VAV.
7220m2. 5 storey and sub-basement. Mainly open plan
Department of Provincial Government (offices).
2 Montreal
39 (38)
double duct, CAV.
68198m2. 15 storeys and sub basements. Mainly open plan.
Department of Provincial Police (jail).
3 Cap-de-la-Madeleine
40 (39)
free cooling, CAV.
3963m2. 3 storeys. Mainly open plan.
Department of Provincial Police (police station).
4 Shawinigan
41 (40)
free cooling, VAV and CAV.
5265m2. 3 storeys. Mainly open plan.
Department of Provincial Government (court).
5 Trois-Rivieres
44 (44)
free cooling, VAV.
10,451m2. 5 storeys. Mainly open plan.
Department of Provincial Government (offices).
6 Longueuil
41 (40)
free cooling, VAV.
14,980m2. 2 storeys. Mainly open plan.
Department of Provincial Government (court).
7 Longueuil
40 (40)
double duct, VAV and free cooling, CAV.
12,500m2. 8 storeys and sub-basement. Mainly open plan.
Department of Provincial Government (offices).
8 Maniwaki
31 (30)
free cooling, VAV
3500m2. 2 storeys and sub-basement. Mainly open plan.
Department of Provincial Government (offices).
9 Gramby
40 (39)
double duct, VAV.
8784m2. 3 storeys and sub-basement. Mainly open plan.
Department of Provincial Government (court).
10 Montreal
40 (39)
free cooling, VAV.
3006m2. 4 storeys. Mainly open plan.
Department of Provincial Government (offices).
11 Hull
42 (40)
double duct, VAV and CAV.
32,345m2. 10 storeys. Mainly open plan.
Department of Provincial Government (court).
12 Montreal
6 (none)
double duct, CAV and free cooling, VAV.
37,325m2. 25 storeys. Mainly open plan.
Private, professional and advertising offices.
Questionnaire
The questionnaire used here was essentially the same as the one used in Townsville
(ASHRAE RP-702 Hot Humid Field Experiment). The subjective survey was divided into
two parts, Background and Online. The Background questions covered areas such as
ASHRAE RP-884 Final Report
Appendix C page 252 MRL Australia
demographics, contextual and psychological factors. The on-line questions were related to
the subjects assessment of their immediate thermal environment at that point in time and
was answered at the time the physical measurements were being taken. Sensation ratings
were based on the ASHRAE 7-pt scale. Thermal acceptability was addressed as a yes/no
response and thermal preference was assessed on a 3-pt scale. Metabolic rating was
based on the ASHRAE 55-92 Standard and ISO 7730 Standard. Met was assessed over
four distinct time periods from which an overall metabolic value was obtained. Clo was
estimated using the ASHRAE Standard 55-92 checklist. Adaptive behaviour questions were
also addressed regarding the subjects perceived control over their thermal environment.
Outdoor meteorological data
The meteorological data recorded in the original field experiment data included; hourly
temperatures, wind speed and direction, relative humidity, daily precipitation, start and stop
times of precipitation and general conditions. These recordings were purchased by the
researchers from the closest met observation site to each building tested. For the purpose
of RP-884 air temperatures and relative humidities at 600 hrs and 1500 hrs were extracted
for use.
RP-884 standardization assumptions
Due to the use of a 150 mm diameter globe with slow response time for measuring globe
temperature, there was uncertainty as to whether or not the instrument achieved thermal
equilibrium within the exposure time. Therefore, all rows where |TAAV-TRAV| >= 2 K were
deleted from the data set before analysis continued. Clo was estimated by the ASHRAE
55-92 Standard so no correction was necessary and allowances for the insulation due to a
chair had been made. The research design was cross-sectional.
ASHRAE RP-884 Final Report
Appendix C page 253 MRL Australia
C.7. Project Title - Richard de Dear’s PhD research project in Australia.
Project researchers and class of investigation
Dr Richard de Dear and Andris Auliciems (University of Queensland). This is a CLASS-2
investigation .
Project file names in the RP-884 database
This project is disseminated as file numbers 11 (Brisbane, summer - HVAC), 12 (Brisbane,
summer - NV), 13 (Darwin, summer “dry” - HVAC), 14 (Darwin, summer “wet” - HVAC), 15
(Melbourne, summer - HVAC) and 16 (Melbourne, summer - NV) in the RP-884 database.
Project publications
de Dear, R. J. and A. Auliciems (1985) “Validation of the Predicted Mean Vote model of
thermal comfort in six Australian field studies.” ASHRAE Trans., V. 91(2), pp. 452-468.
de Dear, R. J. and A. Auliciems (1985). Thermal neutrality and acceptability in six Australian
field studies, Clima 2000, Indoor Climate (P.O. Fanger, editor), Vol. 4:103-108. VVS
Kongress-VVS Messe, Copenhagen.
de Dear, R. J. (1985) Perceptual and adaptational bases for the management of indoor
climate. (St Lucia Queensland: University of Queensland PhD thesis).
de Dear, R.J. and A. Auliciems (1986). Air conditioning in Australia II: User attitudes. Arch.
Science Review, vol. 31, pp. 19-27.
Project location, climate and season
This project was conducted in three major cities, located in three distinct climate zones
across Australia. Samples from both HVAC and NV buildings were taken in Brisbane
(humid subtropical climate) and Melbourne (west coast marine climate) during summer.
Samples were also taken from HVAC buildings in Darwin (tropical savanna or wet/dry
tropics) during the “dry” and “wet” seasons.
Instruments
Wet and dry bulb temperatures were recorded with an Assmann aspirated psychrometer.
Globe temperatures were recorded using a Zeal mercury-in-glass thermometer
ASHRAE RP-884 Final Report
Appendix C page 254 MRL Australia
(manufactured according to British Standard 2842/66) inserted in the centre of a 40mm ping
pong ball painted matt black. Air speeds were measured at three heights within the
occupied zone but only an average was recorded. The anemometers were Kurz 441M with
manufacturer’s claimed accuracy being ±0.03 m s-1.
Sample Buildings
Location Building Code
(blcode)
Sample Size (n)
Climate Controls
(bldgtype)
Floor Area
Occupancy Pattern
Brisbane 1 195 HVAC office building Brisbane 2 102 HVAC office building Brisbane 3 69 HVAC office building Brisbane 4 114 HVAC office building Brisbane 5 84 HVAC office building Brisbane 1 157 NV office building Brisbane 2 124 NV office building Brisbane 3 69 NV office building Brisbane 4 211 NV office building Brisbane 5 49 NV office building Darwin-dry 1 14 HVAC office building Darwin-dry 2 12 HVAC office building Darwin-dry 3 131 HVAC office building Darwin-dry 4 82 HVAC office building Darwin-dry 5 97 HVAC office building Darwin-dry 6 52 HVAC office building Darwin-dry 7 53 HVAC office building Darwin-dry 8 50 HVAC office building Darwin-wet 8 48 HVAC office building Darwin-wet 9 85 HVAC office building Darwin-wet 10 100 HVAC office building Darwin-wet 11 58 HVAC office building Darwin-wet 12 157 HVAC office building Darwin-wet 13 58 HVAC office building Darwin-wet 14 49 HVAC office building Melbourne 1 83 HVAC office building Melbourne 2 243 HVAC office building Melbourne 3 102 HVAC office building Melbourne 4 84 HVAC office building Melbourne 1 126 NV office building Melbourne 2 411 NV office building Melbourne 3 16 NV office building
Questionnaire
Thermal sensation was assessed on the ASHRAE 7-point linear scale. Thermal preference
was registered on a symmetrical 7-point scale (-3, -2, -1, 0, +1, +2, +3). Metabolic
checklists were applied to the last 10 minutes, between 20 and 10 minutes ago, between 30
ASHRAE RP-884 Final Report
Appendix C page 255 MRL Australia
and 20 minutes ago and between 60 and 30 minutes ago. The average metabolic estimate
across the last hour was recorded in the data file.
Outdoor meteorological data
Actual meteorological data (temperature and humidity) corresponding to the date stamped
on each questionnaire were purchased from the Australian Bureau of Meteorology.
RP-884 standardization assumptions
The 7-point preference scale was converted to the McIntyre scale so that votes of -3, -2 and -
1 were “want cooler,” a vote of 0 was counted as “no change,” and votes of +1, +2 and +3
counted as “want warmer.” Clothing insulation was converted from the McIntyre 1980
method to the equivalent ASHRAE (1992) value and 0.15 clo was added for chair insulation
to all cases with sedentary metabolic rates. The research design was cross-sectional which
satisfied the assumptions for RP-884, that all subjects were independent.
ASHRAE RP-884 Final Report
Appendix C page 256 MRL Australia
C.8. Project Title - A field study of thermal comfort using questionnaire software.
Project researchers and class of investigation
Guy R. Newsham, PhD. and Dale K. Tiller D.Phil. (National Research Council Canada). This
is a CLASS-3 field experiment.
Project file names in the RP-884 database
This project is disseminated as file number 17 (winter - HVAC) in the RP-884 database.
Project publications
Newsham, G. R. and D. K. Tiller. (1995) A field study of Thermal Comfort using
questionnaire software. IRC Internal Report. No 708.
Newsham, G. R., D. K. Tiller. (1996) Questionnaire Software to Enable Study of Short-term
Changes in Subjective Reactions to the indoor Environment. IRC Internal Report.
Project location, climate and season
Ottawa, Canada. The location is borderline between humid mid latitude and continental
subarctic. The investigation was performed in winter.
Sample buildings
Building Code
(blcode)
Sample Size (n)
Climate Controls
(bldgtype)
Floor Area and
layout
Occupancy Pattern
1 390 HVAC 3 storey, open plan office on part of 2nd floor.
Federal government. Facilities design work.
2 437 HVAC 7 storey, open plan office on part of 1st floor.
Federal government. Variety of bibliographic tasks.
3 988 HVAC 20 storey, mostly open plan on 7th and part of 9th floor.
Federal government. Variety of administrative, technical and scientific tasks.
4 44 HVAC 3 storey, open plan office on part of 2nd floor.
Federal government. Variety of bibliographic tasks.
Instruments
ASHRAE RP-884 Final Report
Appendix C page 257 MRL Australia
Indoor climatic instrumentation consisted of an ACR “SmartReader” thermistor for
temperature and a solid-state hygrometer to measure humidity. Measurements were made
at waist height only and the variables air speed and globe temperatures were not measured.
Questionnaire
The questionnaire was software based addressing 5 questions: environmental conditions at
the time of physical data collection, sensation/comfort rating on a 7-pt scale, thermal
preference, questions regarding adaptive behaviour and clo estimations. Total clothing
ensemble worn by the subjects was estimated using the ASHRAE 55-92 checklist. Thermal
acceptability and activity or any form of metabolic rating was not provided.
Outdoor meteorological data
Outdoor meteorological data including air temperature and humidity (RH%) was measured
by the campus weather station. Three of the study sites were on the same campus as the
station, the fourth was located 10km away. Meteorological data provided with the original
dataset was that closest to the time when the questionnaire was being answered. From this
information our dayta_15 and dayrh_15 variables were extracted. Also provided in the
original data set was outdoor air temperature and humidity at 8:00am from which our
dayta_06 and dayrh_06 variables were obtained.
RP-884 standardization assumptions
The research design for this study was longitudinal, but it was assumed for the purpose of
RP-884 that all subjects were independent (i.e. assumed cross-sectional). Clo was
estimated using the ASHRAE 55-92 Standard so no corrections were necessary. However
clo was measured at the beginning of the day and so to more closely approximate the total
clothing ensemble at the time of the questionnaire, the clo change variable in the original
data set was used for adjustments. This variable specified at the time of the questionnaire
wether the subject had had a major or minor clothing change (+ - 0.34 clo and + - 0.05 clo)
since the morning. These adjustments were made and then 0.15 clo added for the insulation
provided by a chair to give a total insulation as a separate variable. Age was given as the
end point of a bin, but was replaced with the midpoint value. While metabolic rates were not
recorded, a default value of 1.2 mets was temporarily inserted into the file for the purposes
of index calculation, but then removed from the database.
ASHRAE RP-884 Final Report
Appendix C page 258 MRL Australia
C.9. Project Title - “Thermal comfort in Pakistan.”
This project was part of the 1993 Oxford Brookes University field project for The National
Energy Agency Conservation Centre (ENERCON) agency of the Pakistan Government
investigating the reduction of energy consumption in buildings and an adaptive model of
thermal comfort.
Project researchers and class of investigation
Nicol, J. F., G. N. Jami, O. Sykes, S. Roaf, M. Humpherys and M. Hancook (School of
Architecture, Oxford Brooks University). This is a CLASS-3 field experiment.
Project file names in the RP-884 database
This project is disseminated as file numbers 18 (Karachi, summer - NV), 19 (Karachi, winter
- NV), 20 (Multan, summer - NV), 21 (Peshawar, summer - NV), 22 (Peshawar, winter - NV),
23 (Quetta, summer - NV), 24 (Quetta, winter - NV), 25 (Saidu, summer - NV) and 26 (Saidu,
winter - NV) in the RP-884 database.
Project Publications
Nicol, J. F., G. N. Jami, O. Sykes, M. Humpherys, S. Roaf and M. Hancock. (1994) Thermal
Comfort in Pakistan. Oxford Brookes University.
Project location, climate and season
This study was conducted across five cities in Pakistan including Karachi (Lower Indus
Plain), Quetta (Baluchistan Plateau), Multan (southern Upper Indus Plain), Peshawar
(northern Upper Indus Plain) and Saidu Sharif (northern mountains).
Karachi is the capital of the Sindh province and the largest city in Pakistan in terms of
population and size. Karachi is also a major Arabian Sea Port. Being only 4m above sea
level warm moist air blows in from the Indian Ocean, however this does not often result in
precipitation. Karachi is quite humid compared to the rest of the country and this is borne
out by relatively small diurnal and annual temperature ranges (the monthly mean temperature
varies just 11°C in Karachi and generally by 21°C to 25°C in other parts). Karachi has an
average temperature maxima and minima of 33°C and 27°C respectively in July and 25°C
ASHRAE RP-884 Final Report
Appendix C page 259 MRL Australia
and 13°C respectively in January. Karachi falls under a desert climate classification
despite it’s location in a coastal zone.
Multan is a major city on the southern Upper Indus Plain in the Punjab, surrounded by the
desert region of Pakistan. However recent irrigation projects have resulted in microclimatic
changes which have resulted in increases in rainfall with some associated changes in
temperature and humidity. Historical records of temperature maxima and minima are 21°C
and 6°C respectively in January and 42°C and 29°C respectively in June or 40°C and 29°C
respectively in July. The climate zone for Multan is “desert.”
Peshawar is the capital of the North/West Frontier Province and is at the northern end of the
Upper indus plain at an elevation of 359m. The temperatures in Peshawar are fairly similar
to those of Multan. The average maxima and minima are 17°C and 4°C respectively in
January and in June 41°C and 25°C respectively or 40°C and 26°C respectively in July.
The climate zone for Peshawar is semi desert.
Quetta is the capital city of the Baluchistan province and is situated on the north-western
Afghanistan boarder of Pakistan. The city is located at an altitude of 1692m on a dry desert
plateau surrounded by mountains rising over 2500m high. Due to its elevation it is cooler
than Peshawar and Islamabad, but has considerable temperature fluctuations on a daily and
seasonal scale. The rainfall in Quetta is very low as is its humidity because of the
surrounding desert. Average temperature maxima and minima are 10°C and -2°C
respectively in January and 35°C and 18°C respectively in July. Quetta is classified as
being in a cool semi desert climate zone.
Saidu Sharif is a town in the northern hills at an elevation of about 1000m. Surveys were
carried out in Mingora a “twin town” about a mile from Saidu Sharif. Specific climatological
data for the two towns was not able to be obtained. The main factor however for both towns
are their elevations giving mean temperature maxima and minima of 14.3°C and 2.2°C
respectively in January and 36.4°C and 20.8°C respectively in June. The climate zone for
Saidu can be described as semi desert.
Season - The project was divided into two surveys, one in summer (July 1993) and the other
in winter (December 1993 - January 1994) each extending over about a week.
ASHRAE RP-884 Final Report
Appendix C page 260 MRL Australia
Sample buildings
This table indicates only one building per city in Pakistan. In actual fact there were many
buildings, including residences and offices. In the vast majority of cases, there was only one
subject per building. In many cases the subjects were monitored during occupancy of more
than a single building, making the data incompatible with the RP-884 structure. Therefore,
for simplicity, all buildings within a particular city are treated as a single building.
Location Building Code
(blcode)
Sample Size (n)
Climate Controls
(bldgtype)
Floor Area
Occupancy Pattern
Karachi 1 - summer 1 - winter
190 470
NV residential houses and office buildings
Multan 2 - summer 437 NV residential houses and office buildings
Peshawar 3 - summer 3 - winter
556 513
NV residential houses and office buildings
Quetta 4 - summer 4 - winter
492 425
NV residential houses and office buildings
Saidu Sharif 5 - summer 5 - winter
568 548
NV residential houses and office buildings
Instruments
Indoor climatic instrumentation was recorded by a portable datalogger. Relative humidity
and air temperature were monitored by a Hanna Instruments probe. This consisted of a
polished aluminium sheath 19mm in diameter, containing in its ventilated tip a humidity
sensor (solid-state hygrometer) and a thermistor. The instrumentation measured air
temperature, globe temperature and humidity. The globe thermometer had a 38mm
diameter ping pong ball with appropriate emissivity attached over the sensor. All variables
were measured at subjects’ waist height.
Questionnaire
The questionnaire addressed conditions at time of physical measurements. Time lapse
between instrument measurements and questionnaire response was never more than 10
minutes. Comfort was rated using the 7-pt semantic differential based on Bedford. Thermal
preference was rated on a want to be warmer/cooler descriptive scale and thermal
acceptability questions were not considered. Other thermal environmental parameters
included were air movement, draft and skin moisture. Metabolic activity was based on a
descriptive scale and noted at the time the questionnaire was being carried out. Total
ASHRAE RP-884 Final Report
Appendix C page 261 MRL Australia
clothing ensemble insulation experienced by the subject was estimated using the ISO 7730
checklist and work of McCullough (eg 1985) and others.
Outdoor meteorological data
Daily outdoor maximum and minimum temperatures were obtained for a number of the
centres from the Pakistan Meteorological office for July and December 1993 and January
1994. Where temperatures were not provided they were replaced with climatological data
(monthly means) from the International Station Meteorological and Climate Summary Vol.2
CDROM (ISMCS, 1992). All outdoor humidities were also obtained from this source and
had to be derived from mean dewpoint temperature and mean temperature minima and
maxima.
RP-884 standardization assumptions
The Bedford 7-point thermal comfort scale was mapped directly to the ASHRAE 7-point
thermal sensation scale for RP-884 purposes. The data was presented as subjects in
individual houses, with studies conducted in summer and winter, so the project was of
longitudinal research design. For the purpose of this study all houses in the same city were
considered to be identical buildings, thus it was assumed there was a number of subjects
from one building for each city and the subjects were independent between both the summer
and winter studies. Some indices in the original data set had to be re-defined to conform to
RP-884 standards. Clo was estimated by ISO 7730 (1984) and corrected to the ASHRAE
55-92 Standard using the regression models developed within RP-884. The activity variable
in the original data set was used such that if activity was <= 4 then 0.15 clo was added to the
total clothing ensemble to form another variable (insul), that accounted for the additional
insulation provided by a chair for subjects that were seated. Velocity measurements in the
raw data file indicated a systematic bias that was time-dependent. The original data in all
summer files was found to be less affected and so original data were used. In the winter
files, values >1.5 m/s were replaced with an average. The Multan, Winter field experiment
was omitted from the RP-884 database.
ASHRAE RP-884 Final Report
Appendix C page 262 MRL Australia
C.10. Project Title - Comfort criteria for passively cooled buildings. a PASCOOL
task.
Project researchers and class of investigation
N. Baker and M. Standeven, The Martin Centre for Architecture and Urban Studies,
University of Cambridge, UK. This is a CLASS-2 field experiment.
Project file names in the RP-884 database
This project is disseminated as file number 27 (summer - NV) in the RP-884 database.
Project publications
Baker, N and M. Standeven. (1995) “A Behavioural Approach to Thermal Comfort
Assessment in Naturally Ventilated Buildings”. Proceedings from CIBSE National
Conference, Ch 76-84.
Baker, N. and M. Standeven. (1994) Comfort criteria for passively cooled buildings. A
PASCOOL task. Renewable Energy. V 5. n 5-8 Aug 1994. p 977-984.
Project location, climate and season
This field experiment was carried out in Athens, Greece for the summer season.
Athens has a Mediterranean climate.
Sample buildings
Building Code (blcode)
Sample Size (n)
Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 409 NV residential building 2 276 NV residential building 3 443 NV residential building 4 176 NV residential building 5 187 NV residential building 6 135 NV residential building
Instruments
Indoor room climate instrumentation included: a thermistor to measure air temperature, an
omnidirectional hot-wire sensor to measure air speed, a solid-state hygrometer to measure
humidity and a globe thermometer with 38mm diameter ping pong ball to measure globe
temperature.
ASHRAE RP-884 Final Report
Appendix C page 263 MRL Australia
Local climate instrumentation consisted of: a calibrated sensor array comprising air
temperature thermistor, omnidirectional thermistor anemometer and two hemispherical
globe thermometers, mounted on a headset similar to that of a walkman. Data was logged
on a portable logger allowing complete thermal histories to be recorded for the day,
including time when the subject was away from the room.
The local data (headsets) were attached ot questionnaire responses in the RP-884
database file for this PASCOOL project. In cases where local data were unsuitable or
unavailable, room data were substituted.
Questionnaire
The questionnaire addressed the conditions at the time physical measurements were being
taken. Sensation was rated on the ASHRAE 7-pt scale. Questions of thermal acceptability
and thermal preference where both considered and metabolic ratings were taken. Clothing
insulation was estimated using the ISO 7730 checklist. Adaptive behaviour questions
regarding changes in clothing and adjustment to controls such as opening or closing shades,
blinds or windows and relocations within the room were recorded.
Outdoor meteorological data
Outdoor Meteorological air temperature data was recorded simultaneously with indoor
measurement made. For the purposes of RP-884 outdoor temperatures at 600 hrs and
1500 hrs were extracted. Humidities at 600 hrs and 1500 hrs were obtained from the
International Station Meteorological and Climate Summary (ISMCS, 1992) CDROM.
RP-884 standardization assumptions
This project was of longitudinal research design, but for the purposes of RP-884 subjects
were assumed to be independent (ie. cross-sectional). Clothing insulation was estimated
using the ISO 7730 (1984) Standard, it was therefore necessary to adjust clo to conform to
the ASHRAE 55-92 Standard. Also where the metabolic rate was <= 2 met it was assumed
the subject was seated and so 0.15 clo was added to the total clothing ensemble in these
cases to account for the insulation provided by a chair. The 5-pt variable PRF_VOTE in the
original data was re-coded to our 3-pt McIntrye (MCI) scale. Where air velocity was missing
0.1 m/s was temporarily inserted for the software based index calculation and then removed
from the database.
ASHRAE RP-884 Final Report
Appendix C page 264 MRL Australia
ASHRAE RP-884 Final Report
Appendix C page 265 MRL Australia
C.11. Project Title - Developing indoor temperatures for naturally ventilated
buildings.
Project researchers and class of investigation
I. A. Raja, J. F. Nicol and M. A. Humphreys (Oxford-Brookes University, UK). This is a
CLASS-3 investigation.
Project publications
Nicol, J. F., M. A. Humphreys and I. A. Raja (1995). “Developing Indoor Temperatures for
Naturally Ventilated Buildings”. Proceeding for CIBSE National Conference.
Also see the Full Report.
Project file names in the RP-884 database
This project is disseminated as file number 28 (summer - NV) in the RP-884 database.
Project location, climate and season
The project is located in Oxford, South Britain about 63m above sea level and is situated at
51o 46’ North and 1o 16’ West. The climate of Oxford is typical of the low lying part of the
English midlands and is also influenced by its proximity to the Atlantic. Oxford experiences
one of the warmer maxima in the surrounding area with a mean maximum temperature of
21.7°C in July. The mean minima of 1.3°C in January and February reflects weather similar
to that of the midlands and south-east. This field experiment was completed in the summer
months of August and September and comes under the climate classification of west coast
marine.
Sample buildings
Building Code (blcode)
Sample Size (n)
Climate Controls
(bldgtype)
Floor Area
Occupancy Pattern
1 496 NV School of Architecture plus Biological and Molecular Sciences
2 334 NV Headington Hill Hall.
3 47 NV Tonge building.
ASHRAE RP-884 Final Report
Appendix C page 266 MRL Australia
Instruments
Air temperature was measured using a thermistor. An adapted thermistor probe with a
38mm diameter ping pong ball of suitable emissivity attached, was used to measure globe
temperature. Air speed was registered using an omnidirectional sensor and a solid-state
hygrometer was used to measure humidity. All measurements were taken at waist
(generally desk) height.
Questionnaire
A comfort rating on the 7pt Bedford scale was addressed in the questionnaire as well as
thermal preference. Thermal acceptability was not recorded. Metabolic ratings were taken
at the time the questionnaire was being answered, but covered the 15 minute period before
the questionnaire was completed. Clothing insulation estimates were based on the ISO
7730 checklist with the insulation effects of chair included in the total clothing ensemble of
the subject. Questions of adaptive behaviour and perceived control on a subjects thermal
environment were addressed. Specifically, whether doors, window and curtains or blinds
could be opened and closed as well as the influence of fans and heater that could be
switched on or off.
Outdoor meteorological data
Outdoor Meteorological data was obtained for every 0.25 hours from the Oxford University
Radcliffe Observatory by the original researchers. From this, air temperatures and relative
humidities at 600 hours and 1500 hours were extracted for the purposes of RP-884.
RP-884 standardization assumptions
The research design of this project was longitudinal, but for RP-884 purposes all subjects
were assumed to be independent (ie. cross-sectional). The Bedford 7-point thermal comfort
scale was mapped directly to the ASHRAE 7-point thermal sensation scale for RP-884
purposes. Clothing insulation, estimated using the ISO 7730 (1984) Standard was
corrected to the ASHRAE 55-92 Standard via regression models developed within RP-884.
Allowance for the insulation provided by a chair was incorporated into the total clothing
ensemble by the original researchers only when the subjects reported themselves as seated.
This provided the RP-884 insul variable. To obtain clothing insulation (clo) in isolation, 0.15
clo was subtracted. All rows with missing air temperature were deleted, but where velocity
ASHRAE RP-884 Final Report
Appendix C page 267 MRL Australia
was missing, 0.1 m/s was temporarily substituted and where indoor relative humidity and
metabolic rate were missing, 50% and 1 met respectively were temporarily substituted for
the purposes of index calculations and then removed from the database.
ASHRAE RP-884 Final Report
Appendix C page 268 MRL Australia
C.12. Project Title - Mixed mode climate control: some hands-on experience.
Project researchers and class of investigation
David Rowe. Department of Architectural and Design Science, Sydney University, Australia.
This is a CLASS-2 investigation
Project file names in the RP-884 database
This project is disseminated as file numbers 29 (Summer - Mixed Mode), 30 (winter - Mixed
Mode) and 31 (winter - HVAC) in the RP-884 database.
Project publications
Nothing published yet.
Project location, climate and season
The field experiment was conducted in Sydney, the capital of the state of New South Wales
in Australia. Sydney’s climate is humid and sub-tropical. The project conducted in both
summer and winter seasons.
Sample buildings
Building Code (blcode)
Sample Size (n)
and season
Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 137 - summer 170 - winter
Mixed (hybrid) university offices
2 83 - winter HVAC administration offices
Instruments
RTD devices were used to measure air temperature. No globe temperatures were
measured but mean radiant temperature was provided based on the average of six
orthogonal plane radiant temperatures, areally weighted for the projection area factors of the
human body. Air speed was assessed using an omnidirectional sensor and included
turbulence intensity measurements (> 10Hz). A chilled-mirror dewpoint sensor was used to
measure humidity. All measurements were taken at a single height.
Questionnaire
ASHRAE RP-884 Final Report
Appendix C page 269 MRL Australia
The questionnaire for this project was based directly on that used for the ASHRAE RP-702
Hot Humid Field Experiment in Townsville Australia (see above for de Dear et al., 1994).
Thermal sensation rated on the 7-pt ASHRAE scale was recorded at the time physical
measurements were being taken, along with the other items on the questionnaire that follow.
Thermal acceptability and thermal preference was addressed. Metabolic ratings at the time
of and one hour before the questionnaire were recorded. The total clothing ensemble
insulation was estimated using the ASHRAE 55-92 checklist. Other thermal environmental
parameters considered include air movement.
Outdoor meteorological data
Outdoor Meteorological Data consisting of air temperature and relative humidity at 600
hours and 1500 hours was obtained for this field experiment from Macquarie University’s
Meteorological site, Sydney, Australia.
RP-884 standardization assumptions
The research design for this project was longitudinal and for the purpose of RP-884 all
subjects were assumed to be independent (ie. cross-sectional). Clothing insulation was
estimated from ASHRAE 55-92 checklists so no alterations were necessary apart from the
addition of 0.15 clo to account for the insulation effects of a chair in creating our insul
variable. Throughout the field experiment where mean radiant temperature was not provided
air temperature was entered as a substitute.
ASHRAE RP-884 Final Report
Appendix C page 270 MRL Australia
C.13. Project Title - ASHRAE sponsored RP-462. San Francisco area.
Project researchers and class of investigation
Gail Schiller, Edward Arens, Fred Bauman, Charles Benton, Marc Fountain and Tammy
Doherty (CEDR at University of California, Berkeley). This is a CLASS-1 field experiment
Project file names in the RP-884 database
This project is disseminated as file numbers 32 (summer - HVAC), 33 (summer - NV), 34
(winter - HVAC) and 35 (winter - NV) in the RP-884 database.
Project publications
Schiller, G. E., E. Arens, F. Bauman, C. Benton, M Fountain and T. Doherty. (1988) A Field
Study of Thermal Environments and Comfort in Office Buildings: Final Report--ASHRAE
462. (CEDR:UC Berkeley).
Schiller, G. E. (1990) A comparison of measured and predicted comfort in office buildings.
ASHRAE Transactions, 96(1).
Project location, climate and season
RP-462 was conducted over five locations within the San Francisco Bay area including
Berkeley, San Ramon, Palo Alto, San Francisco and walnut Creek. All five cities are within a
Mediterranean climate zone, but all have different local climates due to their location around
the San Francisco Bay area. San Francisco is located right on the coast, but also very close
to the Bay. Palo Alto is situated further from the coast close to southern end of the Bay and
behind the Santa Cruz Mountains. Berkeley is located across the Bay from the Golden Gate
and Walnut Creek is further inland almost directly east of Berkeley. San Ramon is a similar
but shorter distance from the Bay as Walnut Creek, but instead it is almost directly east of
San Francisco. The field experiments were conducted across both summer and winter
seasons.
ASHRAE RP-884 Final Report
Appendix C page 271 MRL Australia
Sample buildings
Location Buildg Code
(blcode)
Sample Size
(n) and Season*
Climate Controls
(bldgtype)
Floor Area
Occupancy Pattern
Berkeley 1 122 - S 121 - W
NV 236,600 ft2. crowed open plan offices.
San Ramon 2 119 - S 123 - W
HVAC - thermal ice storage and evap. ponds.
2,000,000 ft2. office building
Palo Alto 3 92 - S 101 - W
HVAC (multizone HVAC with EMS)
187,000 ft2. mostly private offices.
San Francisco
4 108 - S 134 - W
HVAC - heat pump mech. system.
191,000 ft2. open plan with private balconies on perimeter.
San Francisco
5 115 - S 132 - W
roof-mounted HV unit, no mech. a.c.,
54,000 ft2. open plan converted factory.
San Francisco
6 123 - S 136 - W
NV 90,000 ft2. open plan and private offices.
San Francisco
7 107 - S 122 - W
HVAC - thermal ice storage, VAV perimeter reheat.
265,000 ft2. open plan and private offices.
San Francisco
8 117 - S 147 - W
HVAC 634,000 ft2. large open plan.
Walnut Creek
9 23 - S 145 - W
HVAC 316,400 ft2. open plan and private offices.
Walnut Creek
10 107 - S 146 - W
HVAC 368,000 ft2. open plan with partitions and private offices.
* S = summer, W = winter in the sample size and season column.
Instruments
Air temperature, air velocity, humidity, and globe temperatures were measured using a
mobile cart at the heights indicated below, with the exception of the one stationary
observation point. Air temperature was measured with a shielded platinum RTD at 0.6m
and shielded type T thermocouples at 0.1m, 0.6m and 1.1m were used. Air velocity was
measured by an elliptical omnidirectional constant temperature anemometer at 0.6m and
spherical omnidirectional temperature compensated anemometer at 0.1m and 1.1m.
Humidity was measured by a chilled-mirror dew point sensor at 0.6m. Globe temperatures
were measured by a type T thermocouple inside a 38 mm diameter table tennis ball (painted
ASHRAE RP-884 Final Report
Appendix C page 272 MRL Australia
grey) at heights of 0.1m, 0.6m and 1.1m on the mobile cart and at 1.1m in the stationary set
up. Other variables measured not of relevance to RP-884 include radiant temperature
asymmetry, surface temperature and illumination.
Questionnaire
Questionnaire responses were collected at the time physical measurements were being
taken. The ASHRAE 7-pt scale was used to determine thermal sensation. The McIntyre
scale was used to assess thermal preference. Thermal acceptability was not addressed.
Metabolic rating and clothing insulation estimates were based on checklists in ASHRAE
Standard 55-81 (1981). The background section of the survey (not necessarily completed
when physical measurements were being made) covered general descriptions of office work
areas; degree of satisfaction with components of their work environment; personal and
comparative comfort and personal subject related information.
Outdoor meteorological data
Outdoor Meteorological air temperature minima and maxima were purchased from the US
National Climate Data Center (NCDC) for sites considered of similar climatic situations to
the study locations. Where a suitable site could not be requisitioned, climatological data was
extracted from the International Station Meteorological and Climate Summary (ISMCS,
1992) CDROM. All climatological humidity data were also obtained from ISMCS (1992).
RP-884 standardization assumptions
RP-884 is the fourth ASHRAE sponsored project in the series RP-462, RP-702 and RP-
821. A lot of the assumptions and standards of RP-462 project have formed the basis for
the later projects including RP-884, thus limited standardisation has been necessary here.
Clothing insulation was converted from ASHRAE 55-81 to the 55-92 standard. 0.15 clo was
added to the total clothing ensemble for the insulation effects of a chair to create our insul
variable. The research design of this project was part longitudinal and part cross-sectional,
but for RP-884 purposes all subjects were assumed to be independent.
ASHRAE RP-884 Final Report
Appendix C page 273 MRL Australia
C.14. Project Title - A field investigation of thermal comfort environmental
satisfaction and perceived control levels in UK office buildings, University of
Liverpool.
Project file names in the RP-884 database
This project is disseminated as file numbers 38 (summer -NV), 39 (winter - NV) and 40
(winter - Mixed Mode) in the RP-884 database.
Project researchers and class of investigation
Ruth N. Williams (The Building Services Research and Information Association, Berkshire,
UK). This is a CLASS-2 investigation
Project publications
Williams, R. N. (1995). A field investigation of thermal comfort environmental satisfaction
and perceived control levels in UK office buildings. Healthy Buildings. Vol. 3 pp. 1181-1186.
Williams, R (1996) “Predicting environmental dissatisfaction in UK offices,
“CIBSE/ASHRAE Joint National Conference, Harrogate UK, VII., pp.167-178.
Project location, climate and season
This project was conducted across three towns/cities in the UK, including Liverpool, St
Helens and Chester. All three come under the west coast marine climate classification. The
study was carried out in summer and winter months.
Instruments
Air temperature was measured using thermistors and an omnidirectional hot bead sensor to
measure air speed. A Envirlog supplied sensor (type unknown) was used to measure
humidity and by attaching 38mm diameter ping pong balls globe temperature was also
measured. Air Speed and humidity were measured at waist height. Air temperature and
globe temperature were measured at all three heights (ankle, waist and head), but provided
to the RP-884 database as a single average.
ASHRAE RP-884 Final Report
Appendix C page 274 MRL Australia
Sample buildings
Location Building Code
(blcode)
Sample Size (n)
and season
Climate Controls
(bldgtype)
Floor Area
Occupancy Pattern
Liverpool 1 19 - summer NV office buildings A&B
St Helens 2 8 - summer NV LC
St Helens 3 140 - summer 31 - winter
NV WH
St Helens 4 121 - winter Mixed (hybrid)
NWB
Chester 5 44 - winter NV CCH
Chester 6 31 - winter NV COM
Chester 7 67 - winter NV ANN
Liverpool 8 36 - winter NV SEN
Questionnaire
The questionnaire addressed both conditions at the time of physical measurements and
typical overall conditions. Thermal sensation was rated using a 7-pt ASHRAE scale.
Thermal comfort was rated using the 7-pt Bedford scale. Thermal acceptability was
addressed but not thermal preference. Metabolic rating was dealt with by asking if the
subject was sitting or standing during most of their work time, from which an estimate was
derived. Clothing insulation estimates were based on the ISO 7730 (1994) checklist with
corrections for the insulation from a chair included. Adaptive behaviour questions of the
subjects perceived control on temperature, humidity, freshness, smell, appearance, lighting,
noise and layout within their working environment was noted.
Outdoor meteorological data
Outdoor Climatological air temperature data at 600 hours and 1500 hours was obtained
from Weather (the journal, for site - Ringway). Relative humidity at 600 hours and 1500 hours
was obtained from the International Station Meteorological and Climate Summary (site -
Liverpool) CDROM.
RP-884 standardization assumptions
The research design of this study was cross-sectional which satisfies the assumption of
independence between subjects for RP-884. Coding conventions for some variables was
ASHRAE RP-884 Final Report
Appendix C page 275 MRL Australia
altered to conform to RP-884 definitions. Clothing insulation estimated using ISO 7730
(1984) checklists, was corrected to follow the ASHRAE 55-92 Standard. The sex (gender) of
subjects was not indicated in the study so an average of the adjusted clo to the ASHRAE 55-
92 Standard for males and female was used in all cases. 0.15 clo was then subtracted from
this corrected clothing plus chair insulation to create our clo variable.
ASHRAE RP-884 Final Report
Appendix C page 276 MRL Australia
C.15. Project Title - Thermal comfort in the humid tropics: field experiments in air
conditioned and naturally ventilated buildings in Singapore.
Project researchers and class of investigation
R. J. de Dear, K. G. Leow and S. C. Foo (National University of Singapore). This is a
CLASS-2 field experiment.
Project file names in the RP-884 database
This project is disseminated as file numbers 41 (summer - HVAC) and 42 (summer -NV) in
the RP-884 database.
Project publications
de Dear, R. J., Leow, K. G. and S. C. Foo (1991) “Thermal comfort in the humid tropics:
Field experiments in air conditioned and naturally ventilated buildings in Singapore”.
International Journal of Biometeorology, Vol. 34, pp. 259-265.
de Dear, R.J., Leow, K. G. and A. Ameen (1991) “Thermal comfort in the equatorial climatic
zone -- Part II: Climate chamber experiments on thermal acceptability in Singapore”.
ASHRAE Transactions, Vol. 97(1), pp. 880-886.
Project location, climate and season
The field experiments were conducted in both summer and winter seasons in Singapore
which is a wet equatorial climate.
Sample buildings
Building Code (blcode)
Sample Size (n) Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 333 HVAC office building 2 583 NV residential building
Instruments
A hot-wire sensor was used to measure air speed. Relative humidity was measured using
an aspirated psychrometer and mercury-in-glass thermometers were used to measure air
and globe temperature. For globe temperature a 0.15m copper sphere was used.
ASHRAE RP-884 Final Report
Appendix C page 277 MRL Australia
Questionnaire
Thermal sensation was rated on the ASHRAE 7-pt scale. Thermal acceptability and thermal
preference was not addressed. Metabolic ratings were taken and clothing insulation was
estimated using the ISO7730 1984 standard. Questions of adaptive behaviour were not
considered.
Outdoor meteorological data
Outdoor Climatological air temperature and relative humidity data at 600 hours and 1500
hours was obtained from the International Station Meteorological and Climate Summary
CDROM (ISMCS, 1992) for Paya Lebar, the closest site.
RP-884 standardization assumptions
The research design was cross-sectional which satisfied the assumptions for RP-884, that
all subjects were independent. Clothing insulation estimated using the ISO7730 1984
standard was corrected to the ASHRAE55 1992 standard. 0.15 clo was added to the total
clothing ensemble insulation for the insulation effects of a chair forming a separate variable
in RP-884.
ASHRAE RP-884 Final Report
Appendix C page 278 MRL Australia
C.16. Project Title - The Steelcase building. Grand Rapids Michigan, US
Project researchers and class of investigation
F. Bauman et al. (CEDR at the University of California at Berkeley).
This is a CLASS-1 field experiment.
Project file names in the RP-884 database
This project is disseminated as file number 43 (winter - HVAC) in the RP-884 database.
Project publications
Project location, climate and season
This project was conducted in winter in Grand Rapids, Michigan. Grand Rapids has a
continental location in the Great Lakes region of North America and has a humid mid-
latitude climate.
Sample buildings
Building Code (blcode)
Sample Size (n) Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 85 HVAC office building
Instruments
The Grand Rapids, Michigan field experiment was not part of the Advanced Customer
Technology Test (ACT2) study but was carried out in an identical format. A cart was set up
with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m. The sensors chosen
were selected to meet the response time and accuracy requirements of ASHRAE Standard
55-81 and ISO Standard 7730 for thermal assessment. YSI series 700 probes with vinyl-
coated tips were used to measure air temperature. Globe temperature was measured by
attaching a 38 mm diameter table tennis ball on the temperature sensors. The balls were
painted grey for correct emissivity. Air velocity was measured by Dantec 54R10
anemometers, which are omnidirectional fully temperature-compensated sensors. Dewpoint
temperature was measured by a General Eastern DEW-10 chilled mirror dewpoint
transducer. All parameters were measured at all three heights except dewpoint temperature
which was only measured at 0.6m. Radiant asymmetry and illuminance where also recorded
but were not essential to the purpose of RP-884.
ASHRAE RP-884 Final Report
Appendix C page 279 MRL Australia
Questionnaire
The questionnaire consisted of an on-line questionnaire, which addressed conditions at the
time physical measurements were being taken and a background questionnaire. The latter
covered subject details such as, health and emotional characteristics, office description,
work area and job satisfaction, environmental sensitivity, plus personal comfort, satisfaction
and perceived control. In the on-line section thermal sensation was rated on the 7-pt
ASHRAE scale. Thermal preference was assessed on a descriptive 3-pt scale. Thermal
acceptability was not rated. Metabolic rate was estimated based on a checklist referring to
the subjects activity in the 15 minutes before completing the on-line questionnaire, using
tables in the ASHRAE Handbook of Fundamentals (HOF, 1985). Clo estimates were based
on responses to the clothing item checklist provided in the on-line questionnaire from the
ASHRAE Standard 55-81 method.
Outdoor meteorological data
Outdoor Meteorological data files are for Grand Rapids, MI, USA for the period January to
February 1992 were bought from the State Climatologist for Michigan by RP-884. The files
supplied had 24 hourly Temperatures (F) and Relative Humidity (%) for the 60 day period
required, from which air temperatures and relative humidities at 600 hrs and 1500 hrs were
extracted.
RP-884 standardization assumptions
The detailed methods and protocol used in ASHRAE RP-462 (and extended to the
ASHRAE RP-702 project described above) were carried out in full for the ACT2 Project.
Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462 little
standardisation was necessary. However, clothing was based on the ASHRAE 55-81
method, and so required conversion into equivalent ASHRAE 55-92 values. 0.15 clo was
then added for chair insulation. The research design of this field experiment was part
longitudinal and part cross-sectional, but for the purposes of RP-884, independence
between subjects was assumed.
ASHRAE RP-884 Final Report
Appendix C page 280 MRL Australia
C.17. Project Title - Sunset Building: a study of occupant thermal comfort in
support of PG&E’s Advanced Customer Technology Test (ACT2) for maximum
energy efficiency
Project researchers and class of investigation
Charles C. Benton and Gail S. Brager (CEDR at University of California at Berkeley). This
is a CLASS-1 investigation.
Project file names in the RP-884 database
This project is disseminated as file numbers 44 (summer - HVAC) and 45 (winter - HVAC) in
the RP-884 database.
Project publications
Benton, C. C. and Brager, G. S. (1994) Sunset Building: Final Report; A study of occupant
thermal comfort in support of PG&E’s advanced customer technology test (ACT2) for
Maximum Energy Efficiency, CEDR.
Benton, C. C. and Brager, G. S. Advanced Customer Technology Test (ACT2) Verifone
Progress Report (CEDR UC Berkeley).
Project location, climate and season
San Ramon is one of 3 location, in which 2 of the 4 components of the ACT2 project were
carried out. San Ramon falls within a Mediterranean climate zone, but experiences local
climatic effects due its location. San Ramon is inland east of San Francisco Bay and almost
directly east of the city of San Francisco. The field experiments were conducted across the
summer and winter months.
Sample buildings
Building Code (blcode)
Sample Size (n) Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 152 HVAC office building 2 133 HVAC office building 3 96 HVAC office building
Instruments
ASHRAE RP-884 Final Report
Appendix C page 281 MRL Australia
A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m.
The sensors chosen were selected to meet the response time and accuracy requirements of
ASHRAE Standard 55-81 and ISO Standard 7730 for thermal assessment. YSI series 700
probes with vinyl-coated tips were used to measure air temperature. Globe temperature
was measured by attaching a 38 mm diameter table tennis ball on the temperature sensors.
The balls were painted grey for correct emissivity. Air velocity was measured by Dantec
54R10 anemometers, which are omnidirectional fully temperature-compensated sensors.
Dewpoint temperature was measured by a General Eastern DEW-10 chilled mirror
dewpoint transducer. All parameters were measured at all three heights except dewpoint
temperature which was only measured at 0.6m. Radiant asymmetry and illuminance where
also recorded but were not essential to the purpose of RP-884.
Questionnaire
The questionnaire consisted of an on-line, laptop-computer based questionnaire, which
addressed conditions at the time physical measurements were being taken and a
background questionnaire. The latter covered subject details such as health and emotional
characteristics, office description, work area and job satisfaction, environmental sensitivity,
plus personal comfort, satisfaction and perceived control. In the on-line section thermal
sensation was rated on the 7-pt ASHRAE scale. Thermal preference was assessed on a
descriptive 3-pt scale. Thermal acceptability was not rated. Metabolic rate was estimated
based on a checklist referring to the subjects activity in the 15 minutes before completing the
on-line questionnaire, using tables in the ASHRAE Handbook of Fundamentals (HOF,
1985). Clo estimates were based on responses to the clothing item checklist provided in the
on-line questionnaire from the ASHRAE Standard 55-81 method.
Outdoor meteorological data
Outdoor Meteorological air temperature data was obtained by request to the National
Climate Data Center (NCDC) for San Ramon and humidity was obtained from the
International Station Meteorological Climate Summary CDROM for the closest available site
(Stockton). From this data air temperatures and relative humidities at 600 hrs and 1500 hrs
were extracted for RP-884 purposes.
ASHRAE RP-884 Final Report
Appendix C page 282 MRL Australia
RP-884 standardization assumptions
This project was conducted based on the format of RP-462 (RP-702). Since RP-884 itself is
based primarily on RP-702 and subsequently on RP-462 little standardisation was
necessary. However, clothing was based on the ASHRAE 55-81 method, and so required
conversion into equivalent ASHRAE 55-92 values. 0.15 clo was then added for chair
insulation. The research design of this project was longitudinal, but for RP-884 purposes all
subjects were assumed to be independent (ie. cross-sectional).
ASHRAE RP-884 Final Report
Appendix C page 283 MRL Australia
C.18. Project Title - The Verifone building, a component of the Advanced Customer
Technology Test (ACT2) project.
Project researchers and class of investigation
Charles C. Benton and Gail S. Brager (CEDR at University of California at Berkeley). This
is a CLASS-1 field experiment.
Project file names in the RP-884 database
This project is disseminated as file number 46 (winter - HVAC) in the RP-884 database.
Project publications
Benton, C. C. and Brager, G. S. Advanced Customer Technology Test (ACT2) Verifone
Progress Report (CEDR UC Berkeley)
Project location, climate and season
This field experiment was conducted in winter in Auburn, California and is one of the
components of the ACT2 project. Auburn has a Mediterranean bordering on high altitude
climate and is located inland and to the north east of San Francisco.
Sample buildings
Building Code (blcode)
Sample Size (n) Climate Controls (bldgtype)
Floor Area
Occupancy Pattern
1 128 HVAC office building
Instruments
A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m.
The sensors chosen were selected to meet the response time and accuracy requirements of
ASHRAE Standard 55-81 and ISO Standard 7730 for thermal assessment. YSI series 700
probes with vinyl-coated tips were used to measure air temperature. Globe temperature
was measured by attaching a 38 mm diameter table tennis ball on the temperature sensors.
The balls were painted grey for correct emissivity. Air velocity was measured by Dantec
54R10 anemometers, which are omnidirectional fully temperature-compensated sensors.
Dewpoint temperature was measured by a General Eastern DEW-10 chilled mirror
dewpoint transducer. All parameters were measured at all three heights except dewpoint
ASHRAE RP-884 Final Report
Appendix C page 284 MRL Australia
temperature which was only measured at 0.6m. Radiant asymmetry and illuminance were
also recorded, but not essential to the purpose of RP-884.
Questionnaire
The questionnaire consisted of an on-line questionnaire, which addressed conditions at the
time physical measurements were being taken and a background questionnaire. The latter
covered subject details such as, health and emotional characteristics, office description,
work area and job satisfaction, environmental sensitivity, plus personal comfort, satisfaction
and perceived control. In the on-line section thermal sensation was rated on the 7-pt
ASHRAE scale. Thermal preference was assessed on a descriptive 3-pt scale. Thermal
acceptability was not rated. Metabolic rate was estimated based on a checklist referring to
the subjects activity in the 15 minutes before completing the on-line questionnaire, using
tables in the ASHRAE Handbook of Fundamentals (HOF, 1985). Clo estimates were based
on responses to the clothing item checklist provided in the on-line questionnaire from the
ASHRAE Standard 55-81 method.
Outdoor meteorological data
An error in dates requesting outdoor air temperature data for Auburn from the National
Climate Data Center (NCDC) resulted in the use of climatological data for both air
temperature and relative humidity at 600 hours and 1500 hours. The data was obtained
from the International Station Meteorological Climate Summary CDROM (ISMCS, 1992) for
the closest available site, Sacramento.
RP-884 standardization assumptions
The detailed methods and protocol used in ASHRAE RP-462 (and extended to the
ASHRAE RP-702 project described above) were carried out in full for the ACT2 Project.
Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462 little
standardisation was necessary. However, clothing was based on the ASHRAE 55-81
method, and so required conversion into equivalent ASHRAE 55-92 values. 0.15 clo was
then added for chair insulation. The research design of this project was longitudinal, so for
RP-884 purposes all subjects were assumed to be independent (ie. cross-sectional).
ASHRAE RP-884 Final Report
Appendix D page 285 MRL Australia
APPENDIX D - CLIMATE CLASSIFICATION
ASHRAE RP-884 Final Report
Appendix D page 286 MRL Australia
ASHRAE RP-884 Final Report
Appendix D page 287 MRL Australia
Figure D.1: The climate classification used throughout the RP-884 database.
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APPENDIX E - CODEBOOK FOR RAW DATA IN RP-884 DATABASE
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RP-884 Variable Coding Conventions variable's
Type of data code name Description of variable and units
Basic blcode building ID code Identifiers sub subject number
age subject's age [years] sex subject's gender [0=male, 1=female] year year
day julian date (jan 1=1, dec 31=365)
time time thermal ash ASHRAE Thermal Sensation Scale [-3, +3] questionnaire prxy_tsa Thermal acceptability defined as -1.5<=ASH<=+1.5
[1=unacc. 2=acc] tsa Thermal Acceptability Question [1=unacc. 2=acc] mci Thermal Preference [1=want cooler, 2=no change,
3=want warmer] vent air movement acceptability [6(very acc), 1(very unacc)] avm air movement preference [3(more), 2(no change),
1(less)] comf General thermal comfort right now [1=very uncomf,
6=very comf] act10 metabolic activity in last 10 minutes [met] act20 metabolic activity between 20 and 10 minutes ago [met] act30 metabolic activity between 30 and 20 minutes ago [met] act60 metabolic activity between 60 and 30 minutes ago [met] met average metabolic rate of subject [met] clo ensemble clothing insulation [clo] upholst insulation of the subject's chair [clo] insul clothing plus upholstery insulation [clo]
Indoor Climate ta_h air temperature at 1.1m above floor [oC] Physical Obs ta_m air temperature at 0.6m above floor [oC]
ta_l air temperature at 0.1m above floor [oC] dewpt dewpoint temperature [oC] prta_b plane radiant asymmetry temperature [oC] tg_h globe temperature at 1.1m above floor [oC] tg_m globe temperature at 0.6m above floor [oC] tg_l globe temperature at 0.1m above floor [oC] vel_h air speed 1.1m [m/s] vel_m air speed 0.6m [m/s] vel_l air speed 0.1m [m/s] turb_h turbulence intensity at 1.1m above floor [frac] turb_m turbulence intensity at 0.6m above floor [frac] turb_l turbulence intensity at 0.1m above floor [frac]
Continue Table.
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variable's Type of data code name Description of variable and units
calculated taav average of three heights' air temperature [oC] indices trav average of three heights' mean radiant temperature [oC]
top average of TAAV and TRAV (operative temperature) [oC]
velav average of three heights' air speed [m/s] velmax maximum of three heights' air speeds [m/s] tuav average of three heights' turbulence [frac] pa vapor pressure [kPa] rh relative humidity [%] et new effective temperature index et* [oC] set new standard effective temperature index set* [oC] tsens two-node tsens index [-1.5, +2.0] disc two-node disc index [-4, +4] pmv Predicted Mean Vote, Fanger's Model [-3, +3] ppd Predicted Percentage Dissatisfied, Fanger's Model
[frac] pd_h Percent Dissatisfied due to Draft at 1.1m height, Fanger
et al [frac] pd_m Percent Dissatisfied due to Draft at 0.6m height, Fanger
et al [frac] pd_l Percent Dissatisfied due to Draft at 0.1m height, Fanger
et al [frac] pd_max Percent Dissatisfied due to Draft, max of all 3 heights,
Fanger et al [frac]
personal PCC perceived control over thermal environ [1=no control, 5=complete control]
environmental PCC_AG aggregate perceived control from PCEC1...PCEC7 control PCS how satisfied are you with PCC [1=very dissat, 6=very
sat] PCEC1 can you open/close windows? [1=yes, 0=no] PCEC2 can you open/close external doors [1=yes, 0=no] PCEC3 can you open/close internal doors [1=yes, 0=no] PCEC4 can you adjust thermostats [1=yes, 0=no] PCEC5 can you adjust curtains/blinds [1=yes, 0=no] PCEC6 can you adjust local heaters [1=yes, 0=no] PCEC7 can you adjust local fans [1=yes, 0=no]
Do you exercise any PCED1 windows [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]
of these options? PCED2 external door [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]
PCED3 internal door [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]
PCED4 thermostat [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]
PCED5 curtains/blinds [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]
PCED6 local heater [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]
PCED7 local fan [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]
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Continue Table. variable's
Type of data code name Description of variable and units
Outdoor Meteorol day15_ta outdoor 3pm (max) air temp on day of survey [oC] Observations day06_ta outdoor 6am (min) air temp on day of survey [oC]
dayav_ta outdoor average of min/max air temp on day of survey [oC]
day15_rh outdoor 3pm (min) rel humid on day of survey [%] day06_rh outdoor 6am (max) rel humid on day of survey [%] dayav_rh outdoor average min/max rel humid on day of survey [%] day15_et outdoor 3pm ET* on day of survey (Ta and rh at time of
daymx_ta) [oC] day06_et outdoor ET* on day of survey (Ta and rh at time of
daymn_ta) [oC] dayav_et outdoor average of min/max ET* on day of survey [oC]
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APPENDIX F - CODEBOOK FOR THE RP-884 META ANALYSIS
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Meta Analysis Codebook Variable Description
authors investigators of the study season season of study country country study carried out in
city city where the study was done seasnum 1 = summer (cooling season), 2 = winter (heating season) dataclas grade of research methods and resulting data (1st, 2nd, 3rd) bldgtype type of building, ie. 1 = climate controlled (HVAC), 2 = free running (NV) and 3
= mixed blcode individual building code
n sample size per building m_taav mean taav s_taav standard deviation taav m_trav mean trav s_trav standard deviation trav m_top mean top s_top standard deviation top
m_velav mean velav s_velav standard deviation velav m_rh mean relative humidity s_rh standard deviation relative humidity m_et mean et* s_et standard deviation of et*
ASH55_92 % indoor climatic obs falling within the relevant ASHRAE 55-92 comfort zone predneut the predicted neutral operative temperature given conditions of vel, rh, insul and
met. deltneut neut_top minus predneut preftemp defined in terms of operative temperature by probit analysis of MCI discrep neut_top minus preftemp m_set mean set* s_set standard deviation set*
m_pmv mean pmv s_pmv standard deviation pmv m_ppd mean ppd s_ppd standard deviation ppd
mpd_max mean pd_max (pd_max being the largest PD of the three heights measured) spd_max standard deviation pd_max m_ash mean ashrae thermal sensation vote s_ash standard deviation ashrae thermal sensation vote m_met mean metabolic rate (met) s_met standard deviation metabolic rate (met)
m_insul mean value of the summed clothing and chair insulation (clo) s_insul standard deviation of the summed clothing and chair insulation (clo) m_clo mean clothing insulation (clo) s_clo standard deviation of clothing insulation (clo)
mpcc_ag mean pcc_ag (pcc_ag is the index of perceived control) spcc_ag standard deviation pcc_ag (pcc_ag is the index of perceived control)
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Continue Table Variable Description
mday15ta mean day15_ta (daily maximum outdoor air temperature degC) sday15ta standard deviation day15_ta (daily maximum outdoor air temperature degC) mday06ta mean day06_ta (daily minimum outdoor temperature degC) sday06ta standard deviation day06_ta (daily minimum outdoor temperature degC) mdayavta mean dayav_ta (mean of daily min and max temperatures degC) sdayavta standard deviation dayav_ta (mean of daily min and max temperatures degC) mday15et mean day15_et (ET* at time of max outdoor air temperature degC) sday15et standard deviation day15_et mday06et mean day06_et sday06et standard deviation day06_et mdayavet mean dayav_et sdayavet standard deviation dayav_et f_mci_2 % frequency when mci = 2 (no change) f_tsa_2 % frequency when tsa = 2 (acceptable) fprxysat % frequency when prxy_tsa = 2 (-1.5<ASH<+1.5) assumed acceptable grad_top gradient of the regression model mean_ash verses dose_top (ashtop)
p_top p value of the regression model testing gradient coefficient = 0 neut_top neutrality for dose_top (mean_ash = 0 in regression model ashtop) rang_top the acceptibility range for dose_top (mean_ash = 1.5 - mean_ash = -1.5 in regr
model ashtop). grad_et gradient of the regression model mean_ash verses dose_et (ashet)
p_et p value of the regression model testing gradient coefficient = 0 neut_et neutrality for dose_et (mean_ash = 0 in regression model ashtop) rang_et the acceptibility range for dose_et (mean_ash = 1.5 - mean_ash = -1.5 in regr model
ashtop). grad_set gradient of the regression model mean_ash verses dose_set (ashset)
p_set p value of the regression model testing gradient coefficient = 0 neut_set neutrality for dose_set (mean_ash = 0 in regression model ashset) rang_set the acceptibility range for dose_set (mean_ash = 1.5 - mean_ash = -1.5 in regr
model ashset) grad_pmv gradient of the regression model mean_ash verses dose_pmv (ashpmv)
p_pmv p value of the regression model testing gradient coefficient = 0 neut_pmv neutrality for dose_pmv (mean_ash = 0 in regression model ashpmv) rang_pmv the acceptibility range for dose_pmv (mean_ash = 1.5 - mean_ash = -1.5 in regr
model ashpmv)
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ASHRAE RP-884 Final Report
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APPENDIX G - AULICIEMS’ ADAPTIVE MODEL DATABASE
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indoor TOP
outdoor temp
neutrality Location active/passive climate controls
Researcher (see Auliciems 1981b)
22.8 19.8 22.7 Melbourne A Ballantyne 20.7 9.5 21.3 Melbourne A Ballantyne 20.4 11.1 20.5 Melbourne A Auliciems 1977
15.2 23.9 Sydney A Hindmarsh 13.3 22.3 Sydney A Hindmarsh 21.6 24.2 Sydney P Hindmarsh 19.4 21.4 Sydney P Hindmarsh 12.4 21 Sydney A Wong 21.3 23 Sydney A Wong
19.5 12.8 20.6 Adelaide A Auliciems 22.6 17.1 23.1 Brisbane P Auliciems 19.6 14.7 21.9 Perth A Auliciems 22.4 8.3 21.3 Armidale A Auliciems
28.1 26.2 Darwin P Macpherson 28.1 27.6 Darwin P Macpherson 28.9 26.2 Weipa P Wyndham
28.3 27.8 25.4 Pt Moresby P Ballantyne 25.9 25.4 Pt Moresby P Ballantyne
26.9 25 Pt Moresby P Ballantyne 26.9 27.2 Pt Moresby P Ballantyne
27.8 27 27.5 Honiara P Woolard 28.2 28.9 26.1 Singapore P Ellis 28.6 27 26.1 Singapore P Ellis 28.8 27 27.3 Singapore P Webb 33.4 33.5 30.1 New Delhi P Nicol 30.3 26.4 26.1 Calcutta P Rao 35.9 33.9 31.2 Baghdad P Nicol 28.8 24.8 25.8 Rio de Janeiro P Sa 24.7 21.3 24.6 Rio de Janeiro P Sa
22 22.5 Toronto A Tasker 22.8 23.9 New York A Gagge 21.5 23.6 Minneapolis A Newton
23.5 12.5 24.4 Portland A Pepler 23.6 12.5 22.1 Portland A Pepler 21.1 3.5 19.8 Swedish Towns A SIB 24.1 10.2 19 Swedish Towns A SIB 23.9 0.6 21.5 Zurich A Wanner 23.5 18.3 23.1 Zurich A Wanner 22.7 2.2 20.9 Zurich A Grandjean 23.2 17.8 21.3 Zurich/Basel/Bern A/P Grandjean 18.1 4.7 18.4 London A Bedford 18.8 6.7 19.2 London A Black 19 17 22.2 London A Black
17.2 5.2 17.5 London A Fox 20.5 10.6 22.4 London A Wyon 19.7 4.7 18.9 London A Angus 21.4 15.9 19.4 London P Hickish 21.1 17.9 21.3 Garston A Humphreys 21.4 3.8 19.9 Garston A Humphreys 21.4 7.7 19.7 Garston A Humphreys 21.4 10.8 19.3 Garston A Humphreys 21.4 14.4 20 Garston P Humphreys 21.4 16.4 20.2 Garston P Humphreys
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