data-driven occupancy patterns · 2020. 9. 17. · mixed methods approach to determine occupants’...

Chair of Smart Architectural Technologies, Department of the Built Environment

Dr. Olivia Guerra-Santin

Data-driven occupancy patterns

What occupancy factors are important to predict building performance?

Building performance and occupants

2

PRE-BOUND EFFECT

REBOUND EFFECT

INFLUENCE OF HOUSEHOLD TYPOLOGY

RELATIONSHIP PEOPLE-

TECHNOLOGY

BEFORE DELIVERY (DESIGN PHASE)

AFTER DELIVERY (USE PHASE)

PERFORMANCE GAP (EXPECTED

VS. REAL)

ACTUAL ENERGY USE

CHALLENGES


3

PRE-BOUND EFFECT

REBOUND EFFECT



TECHNOLOGY




VS. REAL)

ACTUAL ENERGY USE

CHALLENGES

More accurate simulations and

calculations

User understanding of environmental

effects

Design according to user needs and

preferences

User understanding of

building technology

DD SOLUTIONS


4


calculations


effects


preferences


building technology

PRE-BOUND EFFECT

REBOUND EFFECT



TECHNOLOGY




VS. REAL)

ACTUAL ENERGY USE

CHALLENGES

DD SOLUTIONS

SINGLE SENIORTime at homeTemperatureSetbackRadiators

bedroomsRadiators othersVentilation

MORE ENERGY INTENSIVE

LESS ENERGY INTENSIVE

LAR

GER

HO

USE

HO

LD

SMA

LLER

HO

USE

HO

LD

NUCLEAR FAMILYTime at home

TemperatureSetback

Ventilation

Radiators bedroomsRadiators others

SINGLE-PARENTTime at homeTemperature

Setback

Ventilation


THREE ADULTSTime at homeTemperature

Setback

Ventilation


SINGLE ADULTTime at homeTemperature

Setback

Ventilation


ADULTS COUPLETime at home Temperature

Setback

Ventilation


SENIORS COUPLETime at homeTemperature

Setback

Ventilation


Occupants profiles: actual needs and preferences

Statistically determined household profiles

Development of Dutch occupancy and heating profiles for building simulation. Guerra-Santin & Silvester. BRI 45(4), 2017

https://doi.org/10.1080/09613218.2016.1160563

https://doi.org/10.1080/09613218.2016.1160563

6


Statistically determined household profiles

2ndSkin renovation solution

Considering user profiles and occupants’ behaviour on a zero energy renovation strategy for multi-family housing in the Netherlands. Guerra-Santin et al. Energy Efficiency 11(7) 1847-1870.

https://link.springer.com/article/10.1007/s12053-018-9626-8

https://link.springer.com/article/10.1007/s12053-018-9626-8

7

Building monitoring


https://doi.org/10.1016/j.enbuild.2019.109688

Understanding the performance gap in energy retrofitting: Measured input data for adjusting building simulation models. Cuerda et al. ENB 209, 2020

https://doi.org/10.1016/j.enbuild.2019.109688


8


calculations


effects


preferences


building technology

PRE-BOUND EFFECT

REBOUND EFFECT



TECHNOLOGY




VS. REAL)

ACTUAL ENERGY USE

CHALLENGES

DD SOLUTIONS

9

Actual needs and preferences Drivers for behaviour

Mixed methods incl. data mining complemented by walkthroughs, interviews, etc.


Occupancy practices

QUANTITATIVE ANALYSIS(Monitoring data)

QUALITATIVE ANALYSIS(Interviews, etc.)

OCCUPANTS’ BEHAVIOUR PATTERNS FOR BUILDING SIMULATION

UNDERSTANDING HEATING PRACTICES AND BEHAVIOUR

INDOOR PARAMETERS

BUILDING OPERATION

THERMAL COMFORT

VOTES

OCCUPANTS’ BEHAVIOUR

HEATING PRACTICES

HOUSEHOLD DAILY PRACTICES

ATTITUDES

THERMAL COMFORT

PREFERENCES

https://doi.org/10.1016/j.enbuild.2016.08.084

Mixed methods approach to determine occupants’ behaviour – Analysis of two case studies, Guerra-Santin et al. ENB 130 546-566, 2016.

https://doi.org/10.1016/j.enbuild.2016.08.084

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Needs

Drivers for behaviour

Mixed methods incl. data mining complemented by walkthroughs, interviews, etc.


Occupancy practices

ENVIRONMENT

REDUCING ENERGY COSTS

COMFORT

CONVENIENCE

ADJUSTMENT CLOTHING

SHOWER, DRINK TO KEEP WARM

DIFFERENCES COMFORT/ FAMILY MEMBERSPreferences

WORKING CONDITION

data-driven occupancy patterns · 2020. 9. 17. · mixed methods approach to determine occupants’...

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