ieq assessment on schools in the design stage(4)

8/18/2019 IEQ Assessment on Schools in the Design Stage(4)

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IEQ assessment on schools in the design stage

Tiberiu Catalina, Vlad Iordache*

CAMBI Research Center, Technical University of Civil Engineering of Bucharest, Faculty of Building Services and Equipment, Bucharest, Romania

a r t i c l e i n f o

Article history:

Received 8 July 2011Received in revised form

11 September 2011

Accepted 13 September 2011

Keywords:

Indoor environmental quality

Regression models

Energy consumptionBuilding design

a b s t r a c t

Nowadays, the indoor environment quality (IEQ) is growing as a new and very useful index of thebuilding quality. The current literature presents the IEQ assessment based on questionnaires applied for

existing building. The original approach of this study consists in the development of an IEQ index modelthat can be used by architects and engineers during design stage, in order to use it as an evaluationindicator or optimize the building energy consumption versus indoor environmental conditions. Based

on a large database of values resulted from simulations, multiple non-linear regression models wereobtained in order to predict variables such as operative temperature, indoor sound pressure level, indoor

average illuminance and specic energy consumption. A predictive model for IEQ index is proposed asa function of the four quality indexes (air quality, thermal, acoustic and visual comfort). The entire

approach was tested by means of a study case where the impact of windows’ size and type on the IEQ assessment is discussed for a random climate. Also, there are presented detailed results concerning themonthly variation of IEQ and its correlation with the energy consumption. The proposed method proves

to be a fast and useful method to verify architectural and engineering solutions according to the IEQ

estimator. 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Nowadays the indoor environmental quality (IEQ) is not onlyrelated to thermal conditions but it also goes much further, becauseit involves air quality, lighting and acoustics. All these aspects of theindoor environment interact with each other and may have

consequences on the overall indoor comfort and building energyconsumption. When talking about obtaining certain indoor condi-tions it must be also mentioned what are the costs in terms of energy. Current indoor environmental assessment includes four

main aspects, namely thermal comfort (TC), indoor air quality(IAQ), visual comfort (VC) and acoustic comfort (AC) [1].

The IEQ is considered supplementary information on the

building energy performances evaluation procedure because itmay explain the energy consumption [2]. For example, highenergy consumption with a low-energy mark might be justied bya high quality of the indoor environment which has a good indexvalue. Thus the IEQ is essential because it explains better the

energy consumption of a building and its energy classicationbut at the same time it affects the productivity and health of occupants.

Among the variety of constructions and their destination, oneparticular place is occupied by schools where children spend most

of their time. In school building design, efforts are being made inorder to ensure the construction of quality learning environments.Students’ comfort and performance should be a priority in a schooldesign, but a detailed analysis of the energy consumption and cost

effectiveness of the building are also mandatory. In East-Europeancountries, schools design evaluation procedures are missing in theprocess of optimization and assessment of good indoor conditions.Public demand to improve the educational achievement of children

is strong, thus extensive research on the schools environment isessential.

Because of a growing awareness of the indoor environmental

inuence on occupants’ productivity and ef ciency, there is anincreased interest in obtaining feedback from occupants, which isoften obtained by using a questionnaire [1,3,4]. E.G. Dascalaki et al.[5] presented for the rst time a large scale subjective assessmentof IEQ in hospital operating rooms. They found that each of the IEQ

parameters is important and a good value of IEQ improves workingconditions and minimizes complaints from occupants. Poor indoorenvironmental quality is often blamed for causing sick buildingsyndrome [6] and the impact on health is even higher in schools.

The indoor conditions are inuenced by numerous elements,like HVAC systems, building envelope, occupant’s behavior or airinltration. Among these elements, one key piece is the glazing

* Corresponding author. Tel.: þ40 749218162; fax: þ40 212523967.

E-mail address: [email protected] (V. Iordache).

Contents lists available at SciVerse ScienceDirect

Building and Environment

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c om / l o c a t e / b u i l d e n v

0360-1323/$ e see front matter 2011 Elsevier Ltd. All rights reserved.

doi:10.1016/j.buildenv.2011.09.014

Building and Environment 49 (2012) 129e140

mailto:[email protected]://www.sciencedirect.com/science/journal/03601323http://www.elsevier.com/locate/buildenvhttp://dx.doi.org/10.1016/j.buildenv.2011.09.014http://dx.doi.org/10.1016/j.buildenv.2011.09.014http://dx.doi.org/10.1016/j.buildenv.2011.09.014http://dx.doi.org/10.1016/j.buildenv.2011.09.014http://dx.doi.org/10.1016/j.buildenv.2011.09.014http://dx.doi.org/10.1016/j.buildenv.2011.09.014http://www.elsevier.com/locate/buildenvhttp://www.sciencedirect.com/science/journal/03601323mailto:[email protected]


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area, due to its potential to reduce or increase different types of indoor comfort (visual, acoustic and thermal); therefore the glazing

area acts as the primary climate moderator [7].Persson et al. [8] showed that using energy-ef cient windows

would be a better solution than having a highly insulated wall

without windows. The glazing area does not directly inuence thethermal comfort but it affects the energy consumption which isrelated to the indoor conditions [8]. Other research studies wereorientated only toward the impact of glazing on the energy

consumption and thermal comfort [9e12].Most of these research studies are orientated toward the IEQ

assessment using questionnaires on buildings that have been

already constructed and in use. The original approach of thepresent study is the development of mathematical tools to beused for fast and easy IEQ assessment and to assist the architectsand engineers in nding optimal solutions for a new building

design or for an old building rehabilitation. This article aims tobring a new perspective on IEQ assessment in a much easier andaccessible way so that building design engineers could nd optimalsolutions for design healthy and low-energy consumption build-

ings. This paper also attempts to answer a series of questionsregarding the correlated inuence of glazing area on the indoor

conditions:

- Do we have an independent or a simultaneous effect of glazingtype change upon IEQ index? (e.g. larger area of glazingcorrespond to a higher amount of energy for maintaining the

indoor thermal conditions but smallerelectric consumption forthe lighting)

- Would it be possible to counteract a negative effect on the IEQ index with a positive one? (e.g. large area of glazing has

a negative impact on the acoustic comfort but a positive one onday lighting levels)

- What is the overall IEQ index and what is the energyconsumption required for covering that degree of comfort?

In most of the cases, the design stage of new building or therehabilitation of old buildings implies an analysis of the energydemand, but a correct design should also include the IEQ assessment

in this early project stage. Architects and engineers need usefuldirections on building design parameters with an impact on energydemand and the IEQ of the future project. Actually, one of the mostreliable solutions to predict the indoor conditions is the dynamic

simulation of the building in order to estimate the impact of designalternatives and better understand the design problems. Before orduring a project design, several solutions should be proposed andstudied but the lack of time and the complexity of data analysis stop

this processof optimization andresearch.The solution to this problemis to develop common regression models based on large databasevalues obtained from simulations or experimental measurements.

The prediction models simplify the parametrical studies and replacein the initial design phase the numerical simulation tools in order tooptimize the building energy consumption versus indoor environ-mental conditions. This article presents a predictive IEQ model forschools façade design based on non-linear regressions with high

correlations (R2 > 0.9). The outputs of the models are basedon a large

number of simulations using Trnsys software [17]. The regressionmodels can be used for a fast IEQ assessment during the early designstage and can lead to valuable parametric studies on indoor comfortimprovement or energy reduction.

2. Regression models approach

The purpose of the regression analysis is to predict the single

dependent variable (e.g. operative temperature, mean illuminance

level, noise insulation, cooling/heating specic consumption) by

means of a set of independent variables (e.g. windows to oor arearatio, climate coef cient and south equivalent surface). Comparedto neural networks, multiple regression analysis could be an easier

and more practical solution to various problems which arefollowing a constant pattern [13]. It was found that non-linearmodels are substantially more accurate than linear models anda signicant reduction of least square estimator is possible [14]. By

developing a correlation method, it is essential to generate a largedatabase containing many parametric studies. The principle of a,black-box, was used in this part where the inputs and outputs were

rstly identied, then the process continued with the research of

the, black-box, curve-t function (see Fig.1). A, black-box, model of a system is one whose internal structure is unknown but theinputs/outputs are known. Therefore, there is a question of “curve-

tting” by searching the most appropriate function. Accurate

knowledge of the consequence of parameters and of their rela-tionship is essential for optimal and feasible outcome of theexamined function.

In order to acquire the desired variables and to predict the

indoor environmental quality, numerous school design aspectslike shape, heated volume, glazing area or insulation level were

analyzed. These building design variations have an impact on theregression models outputs (operative temperature, mean illumi-nance level, etc) and are considered to be relevant for the study.

There were made several assumptions based on the fact that themodels are meant to be used in school building design. The indoor

heating set point temperature was considered to be of 22 C and forthe cooling of 26 C. During night-time the set-back temperature is

considered to be 15 C. The IEQ assessment was computed fora school time schedule from 8 a.m to 18 p.m during the weekdays.The heat gains are considered to be for 1 student/6 m2 (Degree of activity: Seated, light writing e Sensible heat of 65 Watts and

Latent heat of 55 Watts) and an articial lighting of 10 W/m2duringthe periods when the day lighting is not suf cient to ensure anilluminance level of 300 lx.

2.1. Thermal comfort and energy consumption output parameters

Thermal comfort relates human sensation and perception with

numerous environmental and physical parameters. Two of theenvironmental parameters which have a signicant impact on thethermal comfort are the indoor air temperature (qi) and the meanradiant temperature (qmr). The presence of fenestration systems

adds complexity to the problem of human thermal comfort sincethere is a direct connection with the exterior space. Fluctuations inthe outside temperature and the solar radiation results in a variableinterior wall/glass surface temperature and transmitted solar gain

[15]. These uctuations have a direct impact on the qmr and

implicitly on the operative temperature qo (see Eq. (1)) which is the

output parameter of the thermal prediction model.

qo ¼ hr qmr þ hc qi

hr þ hc (1)

This output of the thermal model is also found to be an input

parameter for the IEQ evaluation in the European Standard 15215[16]. Using Trnsyssoftware [17], a large numberof simulations were

X1

X2

X i

Black-box model

(Curve-fit function)

C o n t r o l l e d i n p u t s

( f a c t o r s )

Y i

Y i=f(X 1 ,X 2 ,….X i)

O u t p u t s

Fig. 1. Black-box model example.

T. Catalina, V. Iordache / Building and Environment 49 (2012) 129e140130


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computed and the resulted data were used to generatethe databaseof values needed in the regression technique. The operative

temperature obtained from thesesimulations was averaged for eachmonth andonly duringthe school time scheduleso that this value tobe the most representative for that period. Other outputs are the

annual specic energy consumption of the building for the heating

qh (kWh/m3/year) and refrigeration qr (kWh/m

3/year) [2]. Theenergy consumption for heating/cooling is evaluated for eachcalculation step (year, month or hour) and the nal energyconsumption represents the sum of theenergy consumptions for all

the time steps inside the heating/cooling period of the year (seeAppendix). Calculation of the operative temperature can be a dif -cult task and in most of the cases dynamic simulations are required

for a more accurate view on this parameter. Using a simpler math-ematical model can be a real solution to fasten parametric studies.

2.2. Visual comfort output parameter

Day lighting contributes to a better IEQ and has a positive effect

on an occupant’s perception of productivity and performance [18].Daylight design requires a number of variable resources, among

which the fenestration is the key piece. Design decisions need to bemade early, so easy models are need to be used for predicting thedaylight potential and to explore different scenarios of buildingdesign [19]. Therequired lighting level is independent of season andits designvaluefor school’s classroomsis of 300lx [16]. Themonthly

average illuminance level (E a) is considered the output parameter

for the visual comfort. This level is established based on dynamicsimulations using Dialux software [20] forone representative dayof each month (e.g. 15th January, 15th February, etc) using on the datasimulated during the school time occupational period.

2.3. Acoustic comfort output parameter

Noise is an essential IEQ physical factor that can contribute to

occupant’s discomfort and must not be avoided in the buildingdesign study. In schools and other study training centers, noise maybe very disturbing and may even weaken the student ’s intellectualperformances [21]. Noise pollution can originate from the outdoorenvironment (through the glazing area), from the ventilation

system (noisy fans, high ventilation rate) or other adjacent interiorspaces (corridors stepping noise or loud talking). The buildingenvelope should ensure required sound insulation against bothairborne noise and structure-borne noise. In general, sound insu-

lation against airborne noise that is expected from the buildingenvelope is of primary importance while the weak parts are thewindows. Numerous studies have analyzed the sound transmissionthrough closed or opened windows and the impact on the room

acoustics [22e24]. Most of them were orientated toward the sound

insulation of the glazing or the impact of street noise on the indoorconditions. American National Standards Institute (ANSI) andEN15215 standard [16] suggest that the background noise in the

classroom should be below 35 dB(A). The indoor acoustical comfortis generally evaluated by using the indoor sound pressure level (L pi)as it was found in different research studies [18,21] and it is

considered the output parameter of the acoustic model prediction.A 100% fulllment of the acoustic comfort is 30 dB(A) while theminimum comfort is of 60 dBA(valuefromwhere the study processcannot be accomplished anymore). In order to calculate the L pia number of steps were followed:

Calculation of the sound attenuation of the opaque structure

Rwall (dB) based on the wall density rwall (kg/m2) and frequency

f (Hz) [25]:

Rwall ¼ 13:5log 10 ðrwallÞ þ 13:5log 10 ð f Þ 22:5 (2)

Calculation of the sound attenuation of the façade (R f ) based on

the surfaces of wall and window and their sound attenuationvalues [22]:

R f ¼ 10log 10 Awall þ Awindow

10

Rwall

10 $

Awall þ 10

Rwindow

10 $

Awindow

(3)

Final sound attenuation (Db) calculated using the indoor

absorptive surfaces (sum of all surfaces of the room S Ai),reverberation time of the room (T r ), room volume and soundattenuation of the façade (R f ) [26]:

Db ¼ R f þ 10log 10

0:161$V

T r $P

Ai

(4)

Indoor sound pressure level L pi based on the Db and outdoorsound pressure levels L po:

L pi ¼ L po Db (5)

The values are obtained in dB for each frequency and forobtaining the weighted value in dBA we must rst do an A-weighting and then do the logarithmic sum of all the frequencyvalues. The model output will be a single global value in dBA but in

order to obtain it, the calculations passed through all those steps,previously showed.

2.4. Indoor air quality output parameter

IAQ, as the nature of air in an indoor environment with relationto occupant health and comfort, is an important parameter whichmust not be neglected when studying the IEQ index. In terms of

occupant’s satisfaction, an acceptable IAQ means a room air inwhich no contaminants have harmful concentration levels and at

least 80% of the people exposed to it do not express any dissatis-faction [1]. According to the European Norm 15215 [16], it is

possible to create a design for different categories of indoor airquality, which will inuence the required ventilation rates. Thedifferent categories of air quality can be expressed in different ways

(combination of ventilation for people and building components,ventilation per m2 oor area, ventilation per person or according torequired CO2 level). It will be considered as output the airow perperson Q air (m

3/h/person) in the following three categories: Class A

e 36 m3/h/person, Class B e 25.2 m3/h/person and Class C. e14.4 m3/h/person. For an optimal usage of these values we willconsider that 36 m3/h/person represent a fulllment of 100% of theIAQ while 10 m3/h/person is the minimum requirement.

2.5. Models input parameters

a) Climate

The rst input of the thermal prediction model is the climate

which was dened using the monthly soleair temperature qsoleair[27] calculated using the hourly values of outdoor dry-bulbtemperature and the global radiation on horizontal. Extremeclimate conditions have been chosen in order to have a wide range

of values. The minimum value calculated was of 8.78 C (Moscowin January) and the maximum of 42.9 C (Abu-Dhabi in August). Thehourly values of outdoor air temperature and solar radiation wereobtained using Trnsys [17] Meteonorm les. For more information

on the qsoleair calculation please check the Appendix.

T. Catalina, V. Iordache / Building and Environment 49 (2012) 129e140 131


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b) Glazing surface and distribution

The window to oor area ratio (WFR) can be translated by thepercentage of the occupied oor area to the total glazing area. Themost appropriate size of a window for energy smart design dependson the building orientation or on the amount of thermal mass in the

internal building materials. A WFR ratio of 15%e18% is recommendedfor conventionalconstructions and it will also balance the energy, the

rst cost and the indoor environmental quality as proposed by the

French Thermal Directive [28]. Buildings implementing passive solarstrategies that use thermal mass and south orientation must beevaluated on an individual basis and may require a different overallWFR to achieve the maximum benet. The parametric study con-

ducted on this input starts from lower values (5%) to higher ratios of 30%glazing of the occupiedoor area. The distribution of the glazingon the building/space façade has an obvious impact on the illumi-nance level andindoor heat gains, butattempting a parametric study

may be a challenging task. It is desired to have a single inputparameter that could dene the glazing distribution and the orien-tation. The south equivalent surface (S es) is used in order to solvea part of this problem and based on the French Thermal Guideline

[28] this surface is dened as follows:

S es ¼Xni ¼ 1

ð Ai$C iÞ (6)

where Ai is the surface of the glazing and C i the orientation coef-cients (see Table 1).

Finally, the used regression input is represented by the fenes-tration size and façade distribution factor F sed dened as:

F sd ¼ S es$WFR ¼Xni ¼ 1

ð Ai$C iÞ$

Ai

Afloor

! (7)

c) Building shape

The shape of a building is an important factor that could inu-

ence the increase/decrease of the energy required for heating orcooling the occupied space. A good solution was found in deningthe building or room geometry and implicitly the heat loss surfacesby using the Rs/v input, which is dened as the ratio between the

sum of all heat loss surfaces that are in contact with the exterior,groundor adjacent non-heatedspacesand theheated volumeof thebuilding/room. The greater the heat loss surface area, the more theheat losses through it, so higher ratios imply high energy demands.

This factor is similar to the compactness factor of a building.

d) Average building insulation value

The building envelope insulation is a critical component of anyfacility because it plays an important role in the energy consump-tion and the regulation of the indoor environment. The FrenchThermal Standard denes the U bui coef cient as the buildingenvelope heat loss coef cient which is the average heat loss of

thermal transmittance through building envelope includingthermal bridges. The U bui is calculated as follows:

U bui ¼

PðU i$ Ai þ ziliÞP

Ai(8)

where U i is the thermal transmittance of building’s components, Aiis the corresponding surface, zi is the linear heat loss coef cient of building thermal bridges and li is the corresponding length.

e) Fresh air change per hour

Other important parameter related to IAQ and energy

consumption is the fresh air change per hour (ACH) which will beaccounted in the heating/cooling consumption regression modelsand not in the operative temperature model.

f) Wall/Window sound attenuation

The façade that separates the indoor space from the outdoornoise is made of an opaque wall and the fenestration area. For

calculating the indoor sound pressure level it is necessary that weknow the sound attenuation (R) for each of these parts (wall andwindow). One input of the acoustic comfort model is the Rwindow(dB), while for the walls it is the surface density of the wall rwall

(kg/m2) that allows the calculation of the Rwall (dB) (see Eq. (2)) foreach frequency (125 Hze4000 Hz). The variation range for thewindows sound attenuation was considered from 15 dB to 40 dB.

- Range 15 dBe30 dB corresponds to old windows type withnon-ef cient joints and high permeability [29]

- Range 30 dBe40 dB corresponds to modernwindows with highquality joints/glass and low permeability [29]

The range of the Rwall depends on therwall, whichcan vary basedon the type of wall (lighter walls rwall ¼ 100 kg/m

2) to heavy walls(rwall ¼ 500 kg/m

2).

g) Outdoor noise level

For calculating L pi (see output calculation steps) the outside

sound pressure levels are needed as inputs. The noise curves (NC)dene the sound pressure levels for each frequency and can varybased on the outdoor noise (e.g. quiet street NC55 with L po ¼ 60 dB

(A)) [30,31]. The studied range of noise curves are: NC55 to NC80(see Table 2).

h) Reverberation time, volume, absorptive surfaces parameter

Like it was show in Eq. (4) the nal sound attenuation dependson the façade sound attenuation, reverberation time, volume andsum of absorptive surfaces. It is considered an input parameter for

the acoustical model the ratio0:161,V

T r ,P

Ai

named from now on Rv/t .

2.6. Model calculation

Several models were tested in order to nd the best t betweenthe simulated data and the model results and it was found that

Table 1

Orientation coef cients.

Windows orientation coef cients

SSEeSSO SSEeESE,

SSWeWSW

ESEeENE,

WSWeWNW

ENEeNNE,

WNWeNNW

NNEeNNW

1 0.85 0.55 0.3 0.2

Table 2

Sound pressure levels for different noise curves.

Sound pressure levels (dB) L po

Frequency 125 Hz 250 Hz 500 Hz 1 kHz 2 kHz 4 kHz dB (A)

NC55 69.8 63.1 58.4 55 52.3 50.2 60NC65 78.5 72.4 68.1 65 62.5 60.5 70

NC80 91.6 86.4 82.7 80 77.7 75.9 85



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quadratic (second-order) polynomial models are the most appro-priate solutions for our problem. To estimate the operativetemperature qop, only the qsoleair, F sed, Rs/v and the U bui have beenused, while in case of the heating/cooling energy consumption, an

extra input was added (ACH) (see Fig. 2).For the visual comfort model, only two inputs were necessary to

compute the monthly average illumination at 0.8 m from the

occupied oor (see Fig. 3).Finally, the last regression model necessary to estimate the

indoor sound pressure level is based on ve inputs as shown in

Fig. 4.The models are valid only in the analyzed range (Table 3) which

was chosen to be applied with a wide range of values (e.g. coldclimates e Moscow, to extreme hot climate - Abu-Dhabi). A highnumber of cases were analyzed for the space insulation U bui due tothe fact that this parameter is calculated as a function of fenestrationparameter F sed, building insulation and building shape ratio Rs/v For

other input parameters it was found that three samples are suf cientto predict the curve tting (rwall, L po, Rv/t, Rwin)

Due to the large number of variations and cases, a considerablenumber of simulations (15.800 samples) were conducted to

generate the database; this database of numerical values (illumi-nance levels, sound pressure levels, operative temperatures, energyconsumptions) was used in the regression analysis to learn

prediction models of the IEQ index.Such a large database of values like in our case represents an

advantage, because the regression techniques could be appliedwith success and good results can be obtained. We found that is

very complicated to calculate each of the indoor parameters (e.g.indoor sound pressure level) and how many software must be usedin order to obtain the nal results. Therefore, developing simpler

tools that include all these aspects is highly needed.The multiple regression shares all the assumptions of correla-

tion: linearity of relationships, the same level of relationshipthroughout the range of independent variable, interval or near-

interval data, absence of outliers, and data whose range is nottruncated. The regression analysis involves nding the best rela-tionship for explaining how the variation in an outcome variable Y i

(e.g. operative temperature qop), depends on the variation ina predictor variable, X i (e.g. soleair temperature qsoleair). Checking

the goodness of t of the model includes the correlation coef cientcalculation R and its squared value R2 but also a particular attentionwas directed toward the residuals plots. The residuals displayeda non-systematic pattern and this shows that the model ts the

data successfully. The learned models (see Table 4) are accurate

(correlation coef cients R > 0.9) and consequently can be used inthe building design stage for new with acceptable errors.

3. IEQ calculation

Calculating the indoor environmental quality of a building/indoor space can be a, painful, procedure, the designer being put inthe situation to deal with numerous theoretical equations and

simulation software for each of the IEQ parameters (operativetemperature, illuminance, sound level). Moreover, this taskbecomes even more dif cult if one has to make a parametric studyon a building design parameter, so the optimization process is

either slow or inexistent. The obtained models are desired to givea simpler and faster solution when architects or building designersare assessing the IEQ. The large number of theoretical equations foreach eld (thermal, visual and acoustic) is successfully reduced to

regression models able to provide the desired inputs for IEQ assessment. At this time, the IEQ calculation procedure as it ispresented in this research paper can be used only for schools orof ces due to the chosen occupational period and indoor heat gains

which are specic to this kind of buildings. The IEQ assessment isbased on the obtained regression models numbers 1, 4 and 5(operative temperature, average illumination and sound pressurelevel) and IAQ index (see Fig. 5).

One of the most important parts in the IEQ assessment is therating based on the parameters that could inuence this calcu-lation. Table 5 summarizes the IEQ parameters limits and theirclassication in different classes and star rating [1]. These limits

were chosen to have the best signicance to the IEQ class (e.g. an

L pi > 60 dBA e will represent a class E (+) acoustic comfortdue to the fact that the studying process cannot be fullledanymore while an Lpi 30 dBA correspond to a class A

(+++++) acoustic environment). The IEQ index can be used forthe same purpose via a star rating system where 5 stars areassigned to the top 10% schools with the best IEQ, 4 stars to thenext 22.5%, 3 stars to the next 35%, 2 stars to the next 22.5% and 1

star to the last 10% [1].The IEQ index (I IEQ ) is calculated based on the thermal comfort

index (I th), the visual comfort index (I v), the acoustic comfort index(I a) and the air quality index (I IAQ ):

I IEQ ¼ I th þ I a þ I v þ I IAQ

4 (9)

with:

Total annual thermalenergy consumption

(kWh/m3 /year)

IEQ input parameter 1(thermal comfort)

Thermal models

Regression Model 2

Regression Model 1

Regression Model 3

op

qh

qr

sol-air

Fs-d

Rs/v

Ubui

ACH

X1 =

X2 =

X3 =

X4 =

X5 =

= Y1

= Y2

= Y3

Fig. 2. Inputs/Outputs of the regression models (thermal comfort/energy consumption).

IEQ input parameter 2(visual comfort)

Visual comfort

Regression Model 4 Ea

sol-air

Fs-d

X1 =

X2 == Y1

Fig. 3. Inputs/Outputs of the regression model (visual comfort).



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I th ¼

qop 21:5 ; I th ¼ 28:57$qop 514qop 24:5 ; I th ¼ 28:57$qop þ 800

I a ¼ 3:33$L pi þ 200I v ¼ 0:33$E avI IAQ ¼ 3:125$Q air 12:5

(10)

The two conditions for the I th calculation are based on theoperative temperature with a maximum value of 100. To makea difference between summer and the winter period the soleair

temperature is used as condition which in our case isqsoleair < 18e19

C (winter period). This value was obtained basedon numerous simulations which indicated that higher values of soleair temperature are translated without any heating demand.

The thermal index must also take into account this condition andmake a difference depending on seasons. For example, if the

qsoleair < 18e19 C and the qop > 21.5 C thenthe I th will be100, orif the qsoleair > 18e19

C and q p < 24.5 C thenthe I th will also take the

value of 100.

4. Study case example

The aim of this chapter is to show the applicability of theproposed IEQ assessment methodology as described in the

previous chapter. Firstly, a comparison between the regressionmodels and the data results from the dynamic simulations will beconducted for a study case. Secondly, a detailed analysis on the IEQ parameters, by using the regression models, will be conducted inone classroomfrom the analyzed school and the data are compared

and discussed. Finally, we will make the IEQ assessment andpropose solutions to improve the IEQ index.

4.1. Building case description

For the case study, it was chosen a single-oor school of a 958 m2 (classrooms e 500 m2, teachers room e 89 m2, corri-

dors e 260 m2, lecture room e 55.1 m2 and toilets 28 m2) situ-ated in Bucharest, Romania (see Fig. 6). The school assembles 174

students and 20 teachers each day with an occupational periodfrom 8:00 am to 18:00 pm during weekdays. The building issituated next to a circulated street (noise curve NC60) and an

adjacent street in its south façade. The reference school is builton a concrete structure having brick walls plastered with cementmortar and presents no thermal insulation. All classrooms andhallways have wooden parquet ooring installed and the entire

building terrace is insulated with polystyrene as well as withbitumen, sand and gravel. All windows have the same surface

( Awindow ¼ 2.8 m2) and they are simple glazed with low thermalresistance (U window ¼ 5.95 W/m

2K). The fresh air change rate is

assumed to be of 2 ach.

4.2. Prediction models analysis

For testing the developed mathematical models, it was selecteda random classroom for which it was conducted a detailed overlookon the IEQ assessment. The energy consumption of the room was

rst analyzed (see Table 6) and then it was found that the errors

between the models and the dynamic simulations are of 6.8% fromthe total annual value. In some cases the errors are higher (May toSep.) but this can be explained by the fact that the absolute errorsare low although the relative error is high. A similar conclusion is

also found for the operative temperature where the errors were lessthan 10%. While analyzing the sound pressure level it was estab-lished that the predictions had comparable values with the simu-

lation results. When checking the IEQ index it can be seen that onlyfor the months of April and October, the errors between the modelsand simulation are higher, but for the mean annual value the erroris more than acceptable (1.9%). Even if in some of the situations the

errors are higher, nevertheless it can be concluded that the modelsare suf ciently accurate to be used in the early design stage forparametric studies.

4.3. IEQ assessment

As previously described, the analyzed reference building is not

insulated, and the window type is single glazing with highpermeability and low sound attenuation and with low thermal

resistance. Many solutions are meant to reduce the energyconsumption and to enhance the indoor conditions inside thebuilding. The classic rehabilitation solutions are the most obvious:replacing the old windows with more ef cient ones and increasing

the thermal insulation of the building. The positive impact on the

I IEQ on this kind of solutions is clear but the exact impact and the

nal benets of such measures remains unknown. There are situ-ations when even the energy measure investment must be placed

into calculation (better insulation may be translated in reducedenergy consumption and I IEQ improvement but higher initialinvestment) and in reaching a more complex situation sometimesa multi-criteria approach may be required to nd an optimal solu-

tion. A building is divided in many thermal zones which have

IEQ input parameter 3(acoustic comfort)

Acoustic comfort model

Regression model 5 Lpi

wall

R

WWR

win

Lpo

Rv/t

X1 =

X2 =

X3 =

X4 =

X5 =

= Y1

Fig. 4. Inputs/Outputs of the regression models (acoustic comfort).

Table 3

Regression models input parameters valid range/samples.

Parameter Units Range/Samples

Soleair temperature qsoleair C 8.78 to 42.9/24

Fenestration parameter F sed m2 0.05e13.75/21

Building shape Rs/v m1 0.11e0.9/4

Building average U-value U bui W/m2/K 0.33e5/84

Air change per hour ACH 1/h 1e6/4

Wall density rwall kg/m2 100e500/3

Window sound attenuation Rwin dB 15e40/3

Window-wall-ratio WWR () 0.25e1/4

Outdoor sound pressure level L po dB (A) 55e80/3Ratio reverberation time/volume and

absorptive surface Rv/t

m/s 0.05e0.38/3



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8/12

overall IEQ index from 71 to 88 with an increase of 24% and anenergy consumption reduction of 25%. These benets come froman increase of the building average insulation of more than 73%,value found not only by a thermal improvement of the walls, roof,

oor or window but also in a better sound attenuation due to themore ef cient glazing.

To have a better view on the impact of different IEQ inputparameters on the index, classroom 1 was chosen as a test room.

Fig. 7 shows the effect of the window to oor area ratio andbuilding room average U-value on the thermal comfort, visual andacoustic index. The advantages of a better insulated room aretranslated in a higher mean radiant temperature during winter

period and a lower one for the summer months. The radianttemperature inuences the operative temperature calculation andimplicitly the I th index. In this case an increase of I th of 18% wasfound between the reference case (non-insulated, single glazing)

Fig. 6. Building case plan and outdoor noise sources.

Table 6

Comparison between predictions/simulations.

Month Energy consumption (kWh) Operative temperature

(C)

Average Illuminance

level (lx)

Indoor Sound Pressure

level (dBA)

IEQ index/rating

Sim. Pr. E. (%) Sim. Pr. E. (%) Sim. Pr. E. (%) Cal. Pr. E. (%) Sim. Pr. E. (%)

Jan 6762 6720 0.6 17.6 17.2 2.4 267 291 9.0 48.4 47.5 2 57/C 59/C 4.4

Feb 5311 5606 5.5 17.9 17.9 0.2 338 324 4.2 48.4 47.5 2 60/C 60/C 0.86Mar 3895 3758 3.5 18.7 19.3 3.3 375 396 5.5 48.4 47.5 2 65/C 70/B 7.7

Apr 1579 1741 10.3 19.9 21.6 9.0 525 518 1.4 48.4 47.5 2 73/B 85/B 16.3

May 396 512 29.5 21.6 23.8 10.0 604 641 6.2 48.4 47.5 2 85/B 85/B 0.6

Jun 101 224 100 23.8 25.5 7.0 638 748 17.2 48.4 47.5 2 85/B 78/B 7.9

Jul 162 452 100 25.0 26.0 4.0 655 784 19.6 48.4 47.5 2 81/B 74/B 8.6

Aug 112 220 100 24.3 25.5 5.1 634 748 17.9 48.4 47.5 2 85/B 78/B 7.8

Sep 352 786 100 21.6 23.2 7.7 554 608 9.7 48.4 47.5 2 85/B 85/B 0.6

Oct 2010 2350 16.9 19.5 20.8 6.7 415 474 14.2 48.4 47.5 2 70/B 80/B 14.2

Nov 3892 4264 9.6 18.5 18.9 1.9 326 374 14.7 48.4 47.5 2 63/C 66/C 5.1

Dec 5809 5972 2.8 17.8 17.6 0.7 276 313 13.3 48.4 47.5 2 58/C 60/C 4.4

Total 30381 32605 6.8 20.5 21.5 4.6 467 518 10.9 48.4 47.5 2.0 72 73 1.9

Sim.- simulated; Pr.-predicted; Cal.-Calculated; E.-relative errors (predicted to simulated).



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and the highly insulated building. At the same time, the visualindex Iv takes lower values (16%) due to the fact that simplewindows have a higher glazing transmittance then the double ortriple glazing windows. Moreover, the major advantage of replacing

the window type is noticed on the acoustic comfort where the I acgoes from39 to 100 with an increase of morethan60% passing fromclass C (+++) to class A (+++++). This signicant increase is theresult of 50% increase of the window sound attenuation (20 dB

Table 7

IEQ assessment and energy consumption.

I IEQ index/rating qh þ qr (kWh/m3/year)

Ref. Sol.1 Sol.2 Ref. Sol.1 Sol.2

Classroom1 72/B ++++ 83/B ++++ 88/B ++++ 89 68 60

Classroom2 73/B ++++ 86/B ++++ 89/B ++++ 87 75 70

Classroom3 73/B ++++ 82/B ++++ 90/A +++++ 84 67 60

Classroom4 69/B ++++ 78/B ++++ 87/B ++++ 88 76 71Classroom5 69/B ++++ 79/B ++++ 87/B ++++ 88 75 70

Lecture room 71/B ++++ 81/B ++++ 89/B ++++ 89 75 69

Teachers room 69/B ++++ 75/B ++++ 84/B ++++ 88 72 59

School 71/B ++++ 81/B ++++ 88 /B ++++ 88 73 66

Fig. 7. Impact of fenestration size and type on the I th, I a and I v index.

Fig. 8. Impact of fenestration size and type on the I IEQ and energy consumption.



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esingle glazing to 40 dB e triple glazing). An increase of glazing

surface has almost no impact on the I th while for the I v (þ20%) andI a (23%) it has a relatively high impact. In some cases choosing the

optimal window size may be a delicate issue especially if one mayprefer a higher visual comfort and less acoustic comfort or viceversa.

The indoor air quality index I IAQ is established based on the

volume of fresh air per person Q air which in this case is of 50 m3/h/

pers representing a 100% of the indoor air quality.The predicted overall IEQ index seems to be more sensitive to

a change of window type than to a change in glazing surface. It was

found that the analyzed classroom could pass from class B (++++)to a class A (+++++) if the WFR went up to 22% and the

U window ¼ 1.4 W/m2K. A signicant drop in the IEQ index is found

for low-insulated windows with low sound attenuation (see Fig. 8).

The energy consumption is always related to the indoor conditionsas it can be noticed in Fig. 8, where the I IEQ index is improved(þ21%) with an increase of the WFR and window type while the

annual specic consumption goes through a signicant drop of 31%.In order to examine the dependence of the predicted overall IEQ

index on the variations of the four input parameters index (I th, I a, I v,

I IAQ ), a U window of 2.95 W/m2K and a WFR of 17% were selected as

nominal conditions. Fig. 9 shows the dependence of the IEQ on thefour inputs and their distribution during the whole year. As ex-pected, the predicted overall IEQ index takes lower values duringwinter period (Decembere January) and summer period (JulyeAu-

gust) because of its sensitivity to an operative temperature28 C. The best conditions were found to beduring spring and autumn periods when the I IEQ index goes up to

95 corresponding to a class A (+++++

) indoor conditions.

5. Conclusions

This research paper tried to answer a series of questionsrelated to the indoor environmental quality in schools and the

impact of window type and size on the overall index. Thebuilding design is generally orientated toward the energyconsumption and the overall IEQ is ignored in most of the cases. Itwas found that actually there is no simple way to assess the IEQ

and in most of the cases the architects and building designengineers have to pass through numerous software simulationsand complicated calculations during the design stage. Moreover,the task of optimization becomes even more dif cult if one wants

to make parametric analysis on several design parameters. In this

article, simple mathematical models have been developed in

order to predict certain indoor parameters (operative tempera-ture, average illuminance, sound pressure level, indoor air

quality) that could inuence the IEQ. In the early building designstage these models can replace dynamic simulations and they areeasier to understand and use than the complex softwareprograms. The models were established using the regression

technique on a large database of values obtained from simula-tions with several input parameters (e.g. soleair temperature,building average U-value, outdoor sound pressure level, rever-beration time, fenestration factor, etc) and outputs (e.g. operative

temperature). An IEQ index was calculated based on the modelsoutputs for each of the indoor comfort type (thermal, acoustic, airquality and visual) and the overall IEQ index was later on

assessed. The prediction models were proved to be accurate

(R2> 0.9) and they were validated with a random building sit-

uated in an East-European city. The analyzed building andclassroom gave interesting insights into the annual IEQ index

distribution and the impact of fenestration type and size on theoverall indoor conditions and energy consumption. It was foundthat increasing the sound attenuation of windows from 20 dB to40 dB could lead to a signicant improvement of the acoustic

comfort index. At the same time, a considerable energyconsumption decrease can be found when increasing thewindows thermal resistance. In the case of the analyzed climate,the fenestration size didn’t have a major inuence on the oper-

ative temperature and implicitly on the IEQ assessment. It can beconcluded that the proposed models are accurate enough whendealing with building design or rehabilitation and can give

valuable information on the IEQ index and energy consumption.

Acknowledgments

The work described in this paper was supported the researchgrants from the Romanian Ministry of Education and Research andUEFISCDI (PN-II-RU-RP-2010-1 code 03). Special thanks to engineer

Adrian Toth for his implication in the project.

Appendix

The annual energy consumption of the building for the heating

Q h (kWh/year) and refrigeration Q r (kWh/year) are calculated as

follows:

Fig. 9. Monthly variation of the I IEQ index.



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Q h ¼ H ðqi qeÞ$t

1000 h$

ðFi þ FsÞ$t

1000

Q r ¼ ðFi þ FsÞ$t

1000 h$

H ðqi qeÞ$t

1000

(11)

where H (W/K) is the building specic heat loss, Fs (W) is the solar

heat gain, Fi (W)are the indoor heat gains, h is the utilization factor

of heat gain/loss and t is the calculation time step (h).The average monthly T soleair temperature can be calculated as

follows:

qsolair ¼

Xni ¼ 1

qei þ a$I hi

ho

n

(12)

where a is the solar radiation absorptivity of the surface ( ), ho isthe heat transfer coef cient for radiation (long wave) and convec-tion (W/m2K), qe is the outdoor temperature [C], I is the global

horizontal solar irradiance (W/m2) and n is the number of hours fora specic month (e.g. n ¼ 744 for January). Unless already knowna default value of 0.6 can be taken for the solar radiation absorp-tivity and of 17.78 W/m2K for the ho. Table A1 shows the soleair

temperature calculated for different cities around the world.

A calculation example of the F sed is presented:Let’s considered a room of 60 m2 oor area with two external

walls of 25 m2 each orientated South and respectively North with10 m2 windows on either of them. The south equivalent surface iscalculated using Eq. (6) with A1 ¼ 10 m

2 and C 1 ¼ 1 and A2 ¼ 10 m2

and C 2 ¼ 0.2 so S es is equal to 12 m2. Further on the glazing surface-

facade distribution is calculated with Eq. (7) and the obtained valueis 3.96 m2, with WFR ¼ 0.333.

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Nomenclature

a: solar radiation absorptivity of the surface () Ai: surface of element i (m

2) A oor : surface of the oor (m

2) ACH: air change per hour (vol h1)C i: orientation coef cient ()Db: room sound attenuation (dB)E: relative errors (%)E av: monthly average illuminance (lx)

f: frequency (Hz)hc : convective heat transfer coef cient (W m

2 K1)hr : linear radiative heat transfer coef cient (W m

2 K1)Q air : volume of fresh air (m

3 pers1)Q h: heating energy demand (kWh year

1)Q r : cooling energy demand (kWh year

1)F sed: fenestration size and façade distribution factor (m

2)qh: specic heating energy demand (kWh m

3 year1)qr : specic cooling energy demand (kWh m

3 year1)I IEQ : indoor environmental quality index ()I IAQ : indoor air quality index ()

Table A1

Monthly soleair temperature for different cities.

Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Paris 5. 0 6.3 10.5 15.5 20.1 23.5 26.8 25.5 20.6 14.6 8.1 5.9Moscow 8.8 5.9 1.4 11.0 20.5 24.0 25.1 22.1 14.4 6.9 0.5 5.3

Abu-Dhabi 23.8 25.4 30.7 34.6 39.9 40.5 42.2 42.9 39.8 36.3 29.8 25.7

Bucharest 0.7 2.7 9.1 17.6 24.4 29.3 30.8 29.3 22.7 14.8 7.2 1.6Londo n 4. 6 5. 7 8.7 13.3 17.9 21.3 23.7 22.1 17.9 13.3 7.6 5.3

Ber lin 0. 7 2. 3 7.5 13.7 21.2 24.6 26.0 24.5 18.9 12.4 5.8 2.1

Barcelona 12.1 13.9 17.6 20.4 25.3 29.5 33.3 31.8 27.6 22.0 16.0 12.8

Shanghai 6.5 8.9 13.2 19.5 25.7 30.4 35.5 33.9 29.6 22.3 15.3 9.4

Helsinki 5.2 5.2 0.8 8.7 17.7 22.3 24.3 20.5 13.5 6.7 0.7 3.8

Rome 10.6 12.5 16.4 20.6 26.3 30.5 33.9 32.4 27.5 21.6 15.1 11.6

Vancouver 4.2 7.1 10.6 14.7 20.7 24.1 26.2 24.7 19.5 12.9 7.7 4.5Sydney 31.4 30.5 27.3 23.0 19.7 16.4 15.9 18.2 21.8 26.1 28.2 30.4

Seattle 5.8 8.9 11.3 15.3 20.8 23.7 26.4 25.2 20.8 14.5 8.9 5.9


http://www.dial.de/http://www.dial.de/


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I th: indoor thermal comfort index ()I a: indoor acoustic comfort index ()I v: indoor visual comfort index ()li: length of thermal bridge (m)L po: global outdoor sound pressure level (dBA)L pi: global indoor sound pressure level (dBA)Rwall: wall sound attenuation (dB)Rwindow: window sound attenuation (dB)R f : façade sound attenuation (dB)Rs/v: ratio heat loss surface to volume (m

1)

S es: windows south equivalent surface (m2)R: correlation coef cient ()Pr: Predicted valuesqsoleair : monthly mean soleair temperature (C)qo: operative temperature (

C)qmr : mean radiant temperature (C)

qe: exterior air temperature (C)qi: indoor air temperature (C)T r : room reverberation time (s)U: element thermal transmittance (W m2 K1)U bui: building average U-value (W m

2 K1)V: room volume (m3)Sim: Simulated valuesWFR: window to oor surfaces ratio ()WWR: window to wall surfaces ratio ()

X i: regression models input parameters ()

Y i: regression models output parameters ()

Greek symbolsa,b,d: regression models coef cientsrwall: density of the wall (kg m

2)zI : linear thermal bridges coef cients (W m

1K1)


ieq assessment on schools in the design stage(4)

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