International Journal of Architecture, Engineering and ConstructionVol 5, No 2, June 2016, 61-71

Toward a Sustainable World Institutional Buildings

Dalia Salem∗ and Emad Elwakil

School of Construction Management, Purdue University, Indiana, USA

Abstract: Buildings’ energy use demonstrates significant discrepancies between the actual energy used and whatis predicted. Occupant behavior is one of the most significant factors contributing to these discrepancies. Lightingis the major energy consumption in institutional buildings. Most of the studies have focused predominantly on thefactors that influence occupants’ usage of the artificial lighting have neglected the occupant performance. Thisstudy aims to understand and build a model for occupants’ performance by investigating the influence of preferredlighting factors on daylighting intensity in the office space. The data was collected from different institutionalbuildings using a Delphi technique. The developed model will be used for predicting the occupants’ performanceusing Regression analysis technique. The model has been validated with AVP of 93.62% and R2 of 0.83. Thedeveloped research/model benefits both architects and practitioners to design the appropriate workplace to enhanceoccupant performance and energy efficiency.

Keywords: Occupants preferences, regression analysis model, lighting electricity consumption, institutionalbuildings

DOI: 10.7492/IJAEC.2016.007

1 INTRODUCTION

The building sector contributes 41% of energy usagein the United States (EPA 2013), and 37% in theEuropean Union (Pérez-Lombard et al. 2008). Duringthe building’s operating stage, more than 80% of theenergy used occur during this time and contributes torisks of global warming (Azar and Menassa 2011b) asshown in Figure 1.

Figure 1. Buildings Share of U.S. Primary EnergyConsumption (2006)

In commercial buildings, most of the energyconsumption is attributed to lighting (25%), followedby space heating and cooling (25%), and ventilation (7%)

(Azar and Menassa 2011a; C2ES 2009; Guo et al. 2010;Ihm et al. 2009).

The majority of people spend 90% percentage of theirtimes indoors, meaning that the indoor environmentaffects their performance (EPA 2009). Buildingprofessionals’ have a significant role in reducing energyconsumption as well as having a role in maintaining thecomfort of the occupants. Artificial lighting systems area major consumer of energy in buildings and contributesignificantly to building cooling load. Daylighting hastwo roles; contributing to the overall environmentalquality of buildings as well as saving energy (Al-Ashwaland Budaiwi 2011). Before the 1940s, daylightingwas considered the main light source in buildingsdesign. Recently, in sustainable buildings, daylightingis considered in regards to energy and environmentalaspects (Edwards and Torcellini 2002). In the case ofoffice lighting, the switching patterns along with theoutside conditions are at the core of investigation fromthe occupants’ behavior point of view. One of the studieshas culminated into the fact that as much as 40% energyconservation can be realized if natural light is relied uponcompared to using artificial light (Bourgeois et al. 2006).

Therefore, the scope of the present study is toinvestigate the lighting preferences for institutionalbuildings and develop a framework for predictingoccupant’s performance.

*Corresponding author. Email: dsalem@purdue.edu

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2 RESEARCH OBJECTIVES

The main objective of this research is to builda prediction model of occupants’ performance forinstitutional buildings. This objective can be brokendown into the following sub-objectives:

1. Identify and study the preferred lighting factorsthat might affect occupants’ usage of artificiallighting in the workplace in institutional buildings;

2. Collect data from a survey from a real workplace;3. Develop a model/framework based on these factors

using regression analysis technique;4. Validate the developed model/framework;5. Test the effect of each lighting factor on the

occupants’ performance using sensitivity analysis.

3 BACKGROUND

The most significant factors that influence the energyand indoor environmental performances of buildings areoutdoor/indoor climate, building characteristics, andoccupant behavior. The most important factor is humanbehavior, followed by building design. Indeed, there isoften an obvious discrepancy between real total energyuse in buildings and what is predicted. The reasons forthis gap are a general need to understand the role ofhuman behavior within the buildings (Fabi et al. 2011).Several studies (Carrico and Riemer 2011; Dietz et al.

2009; Henryson et al. 2000) have taken two differentapproaches which can be used to reduce buildings’energy use: first, the technological approach deals withmore energy efficient building systems and equipme-nt. Second, the behavioral approach focuses onunderstanding building occupant presence and behaviorto measure actual energy consumption and develop bestpractices to encourage conservation (Azar and Menassa2011b).

3.1 Occupants Behavior

Occupants behavior is defined as: “the result ofa continuous combination of several factors crossingdifferent disciplines”. The factors affecting occupantinteractions with building control systems are classifiedinto external and internal factors. The external factorswhich are related to the building science area (e.g.outdoor and indoor temperature) can be categorized intwo categories: the physical environment and the context.The internal drivers concern the social science area canbe defined into three categories: physiological, socialand psychological. These external and internal factorsinfluence occupant behavior, defined as “drivers”. Driverscan be defined as: the reasons leading to a reaction inthe building occupant and suggesting him or her to actas shown in Figure 2.

3.2 Occupants Interactions with IndoorEnvironmental Controls

Several studies have investigated occupants’ preferencesof the windows in their workplace; window size, positionin the walls, and its degree of transparency (Galasiuand Veitch 2006). Many studies investigate theoccupants interactions with the lighting system withoutindicating the occupants satisfaction or performance inthe workplace.These models predict how occupants interact to the

lighting system depending on the lighting intensity,the occupant’s schedules and the surrounded factors topredict the occupant’s use of lighting, and, therefore,predict the lighting energy consumption as a result(Bourgeois et al. 2006; Reinhart 2004). Occupantbehavior affects, directly and indirectly, the building’senergy use in several ways; turning on/off office lighting,opening/closing windows, ventilation, turning on/off

Figure 2. Drivers influence occupants’ behavior (Fabi et al. 2011)

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heating, air conditioning (HVAC) systems, and settingindoor thermal, acoustic, and visual comfort criteria(Hong 2014). Occupant behavior is responsible foruncertainty in the prediction energy consumption inbuildings due to its complexity and diversity (Hong 2014).Mahdavi et al. (2008) studied control-oriented

occupant behavior in few office buildings in Austria.His conclusion determines the possibility of identifyingcertain patterns of occupant behavior as a functionof indoor and outdoor environmental parameter inrespect to luminance and irradiance (Mahdavi et al.2008). Another study evaluated different blind controland lighting systems considering four type of occupantbehavior in office buildings in Belgium. The resultsshow that the energy savings of daylight dimming systemdecreased by 10% when considering occupant behavior(Parys et al. 2009).

3.3 Behavioral Modeling

Previous studies of occupant behavioral models are basedon predicting individual action, which take into accountcontrols for the internal environmental in respect tothe physical conditions. On lighting control behavioralmodels, Hunt (1980) developed a prediction stochasticmodel of manual lighting control based on a fieldstudy. The model calculated the probability of switchingpatterns on arrival and for the intermediate switch-onevents. Newsham (1994) used a modified version ofHunt’s model to predict the lighting switching patternseither in the morning or after lunch if the minimumluminance level is under 150 lux, and not considered aswitch on events on a period of occupation. Newshamet al. (1995) developed Lightswitch model, a field-basedstochastic approach in office buildings in Ottawa, Canada,which predict manual lighting control based on theoccupants’ arrival, departure, and temporary absenteeismin workplaces. Reinhart (2004) developed the stochasticLightswitch 2002 to predict dynamic personal control ofelectric lights and blind systems. Reinhart categorizedthe users as either active or passive users. He defined theactive users as “someone who actively seeks daylighting,rather than relying on artificial lighting as would apassive user”. Lightswitch model has some limitationsconcerning seating positions, the location of controls.Bourgeois et al. (2006) integrate the Lightswitch 2002model with the ESP-r, an energy simulation programof the whole building to develop his model SHOCC,a sub-hourly occupancy-based control model. Resultsshow that nearly 40% of energy conservation can beattained if users depend on natural lighting rather thanartificial lighting. Several earlier studies (Kolleeny 2003;Wong et al. 2009; Paul and Taylor 2008; Vilnai-Yavetzet al. 2005; Kamaruzzaman et al. 2010) studiedvarious factors that have a significant effect on occupants’performance. The factors include exposure to daylight,nature, temperature, air quality, odors, noise, and others.As a result, different models have emerged to assessthe previous factors; such as BREEAM in the UK,and LEED in the US. Salem et al. (2015) studied

described the preferred lighting factors and examined theeffects of environmental, physical, and daily activities onoccupants’ performance and developed a model whichutilized to predict the occupants’ performance usingRegression analysis technique. The model is basedon data was collected from institutional buildings inPurdue University using a questionnaire. There are14 factors that affect the occupants’ performance whichare not applicable. The previous model is based on aquestionnaire that distributed in institutional buildingsin Purdue University only, making it not applicable forthe various institutional buildings. The model overlookedthe diversity of institutional buildings and had a lot offactors (Salem et al. 2015).It can be determined here that the previous studies

investigated the occupants’ behavior inside the workplaceand their interactions with the environment; that is,the impact of the interactions on energy consumption.These studies, however, seem to have overlooked theeffect of lighting preferences in the workplace due to thedifference in environmental, physical, and the policies onthe occupant performance.The use of window blinds and its associated electricity

consumption is not treated in this study, where thefocus is rather on the effect of the various factors(environmental. Physical, activities, and policies) ondaylighting intensity and the artificial lighting switchingpatterns behavior of the occupants.

4 FACTORS AFFECT OCCUPANTSUSAGE OF LIGHTINGINCORPORATED IN THE CURRENTRESEARCH

Based on the above review of the literature and focusingon the institutional buildings, the lighting preferences ininstitutional buildings that affect the lighting intensityin the workplaces are identified and selected as shownin Table 1. These factors are considered in the presentstudy. Fourteen factors are incorporated in this research,which represents the environmental, physical, activities,and policies factors. The factors that influence occupants’usage of lighting are hard to quantify and thus, aqualitative approach has been followed.The factors selected to be incorporated in Regression

analysis model are clustered into four main categoriesand their factors, as shown in Figure 3. The fourmain categories include environmental, physical, usersand tasks that lighting required. Each category includesseveral factors.

5 RESEARCH METHODOLOGY

To achieve the objectives of this research, several stepsare accomplished as shown in the schematic diagramFigure 4. The framework for this project consistedof 5 main steps. It started with a comprehensiveliterature followed by data collection, which in itselfconsists of two parts studying the lighting factorsthat affect the occupants usage and a semi-structured

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Table 1. Lighting preferences factors in institutional buildings (Salem et al. 2015)

Category Variables Description

Environmental factors

OrientationWell-orientated windows maximize daylightingthrough building facades, reducing the need forartificial lighting.

Time of day Time of day affects the lighting intensity; i.e. beforenoon, at noon, afternoon

Sky condition The brightness of the sky; i.e. Full Daylight, OvercastDay, Dark Day

Glare Glare causes difficulty in seeing things in the presenceof bright light, such as direct or reflected sunlight

Physical factors

Window size to wallratio

The window size in regards to the size of the wall,expressed as a ratio

Glazing color The effect of window glazing color on the daylightingintensity in the workspace

Seating positionregarding the window The position of the seat to the window

Lighting locationcontrol regarding theseat

The capability of controlling the artificial lighting inthe workspace

Policies and incentives

Word of mouth (Co-workers in the same space, influencing each other’spreferences)

Energy awarenesscampaigns

(Campaigns that increase awareness of energy and itsimpacts)

Financial incentives (Monetary or other material incentives for reducingenergy use)

Feedback techniques (Employers providing workers feedback on their energyuse behaviors)

Figure 3. Hierarchical factors affect occupants’ usage of lighting

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questionnaire. An open-ended interviews was used toidentify the occupants’ artificial lighting preferences dueto environmental, physical and activities. A regressionanalysis model was developed using model informationdata, which then underwent a verification process. Thenext part of the research methodology was to develop adaylighting usage scale which would guide the architectsto the best design for their office buildings. The modelwas used to assess daylighting efficiency in private andtwo-person offices and then develop lighting behavioralmodeling using Regression Analysis. The last section ofthis paper addresses conclusions and future research.

6 DATA COLLECTION

After identifying the preferred lighting factors thatmay affect the occupants’ performance, a questionnairewas prepared to assess the effect of these factors onoccupants’ performance. The data was collected via aquestionnaire collected from 37 occupants in differentinstitutional buildings. The questionnaire was designedusing Delphi methodology to identify factors that affectlighting intensity in the workplace and then to predictthe occupants’ performance in an abstract approach. Thecollected data consisted of the weights of various factorsincorporated in the model and the performance of eachfactor.This paper presents findings from a web-based survey

on the current use of building design. Sixty individualsfrom different institutional buildings were requested to fillin the questionnaire. These people were faculty and staffmembers worked in offices with or without windows. Thetotal respondents who completed the questionnaire was37, about 62% of the total who received the questionnaire.

7 LIGHTING PREFERENCES MODELSBUILDING

Regression analysis was used to develop the predictionmodel for the occupants’ performance in the workplace

regarding the lighting preferences. The model wasdeveloped based on the previously specified/selectedlighting preferences.

7.1 Regression-Based Occupants PerformanceModel

Regression analysis is a statistical tool that utilizes forthe investigation of the relationship between two or morequantitative or qualitative variables so that the responsevariable can be predicted from the predictor variables.The model can be simplified as shown in Equation 1 (Al-Zwainy et al. 2013):

Yi = β0 + β1Xi + εi (1)

whereYi is value of the response variable in the ith trial,β0 and β1 are regression parameters,Xi is the value of the predictor variable in the ith trial,and εi is the random error.

In multiple regression models, we used more than onevariable to predict the behavior of the dependent variable.The equation that was used was shown in Equation 2(Elwakil 2011):

Yi = β0 + β1Xi1 + β2Xi2 + ...+ βp−1Xip−1+ εi (2)

MINITAB has been used to develop the regressionmodel. MINITAB is a general statistical tool that hasa wide range of capabilities of basic and advanced dataanalysis, such as analysis of variance, basic statistics,correlation and regression, and multivariate analysis(Minitab 2006 ). We used step-wise regression analysisto select the best number of variables in the model.MINITAB was used to develop a regression model for

occupants’ performance as a response to the previouslyselected lighting predictors in the commercial buildings.Four selection criteria were utilized to distinguish betweendifferent proposed models. These criteria were R-square,

Figure 4. Research methodology

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adjusted R-square, predicted R-square, mean square error(S or MSE), and Mallow’s Cp. The best model that couldrepresent the collected data set was selected according tothe largest R-square predicted R-square and adjusted R-square, the minimum mean square error (MSE), and theclosest Cp to the number of independent variables. Hence,if the selected model had only eight independent factors,the best model was the model with Cp value close toeight. These criteria were considered, in selecting the bestmodel that could meet these requirements. Therefore, thebest model that could represent the collected data sethad the highest R2 of 92.09% and adjust R2 of 89.07%,Predicted R-square of 83.33%, the Cp value of 7.9 closeto 8 (i.e. number of variables), and the minimum MSEvalue of 3.8566. The best-obtained formula describing theoccupants’ performance is shown in Equation 3.

Y =53.48− 0.2124X1 − 0.4946X2 − 0.2315X3

+ 0.3869X4 − 0.2731X5 + 0.3038X6 + 0.1022X7

+ 0.2699X8

(3)

In equation the dependent variable (Y ) denotes theoccupants’ performance expressed as a percentage andXs denote the lighting factors that shown in Table 2 andthe subscript refers to their numbers in the table. Forexample, X5 denotes number 5 which is “Lighting locationcontrol”. The built model is checked for its statisticalvalidity. The main diagnostics in this regard are R-square(coefficient of multiple determination), F-test, and t-testfor model coefficients.The best subset analysis determines the best-fit

regression model that can be constructed with thespecified number of variables. As shown in Table 3 thebest subset is a combination of eight instead of elevenvariables, even though the same R2 is slightly higher foreleven variables because a lower number of variables in amodel is better at presenting the model effectively.

7.2 Validation of Developed OccupantsPerformance Model

The validation process is used to guarantee that thedeveloped models best fit the available data. In orderto determine the efficiency of the developed model toderive real-world results, the model is tested statistically,logically, and practically. In this study, the collected datawere divided into two data sets, model building (80%)

and validation (20%). The validation data set, that is20%, selected randomly and kept away while modelingthe regression analysis. After developing the regressionanalysis model, the validation dataset was used to testthe capability of the developed lighting factors modelto predict the occupant’s performance. The developedmodel was validated by comparing the predicted resultswith the actual values of the validation data set.

AIP =

n∑i=1

{1− I(Ei/Ci)I} ∗100

n(4)

AV P = 100−AIP (5)

where AIP is the Average Invalidity Percent, AV P isthe Average Validity Percent, Ei is the ith predictedvalue, Ci is the ith actual value, and n is the numberof observations.Equation 4 expresses the average invalidity, which

indicates the prediction error while Equation 5 presentsthe average validity percent. The AV P values for thedeveloped performance prediction model using regressionis 93.62% as shown in Table 4. These values indicate thatthe obtained results are satisfactory.

8 SENSITIVITY ANALYSIS

The effect of each lighting factor on the occupants’performance was tested using the sensitivity analysis.Sensitivity analysis is determined using the developedperformance regression model. Each factor is assumedto change within the scale (0-100) followed by predictingthe occupants’ performance corresponding to the changein each lighting factor as shown in Table 5.In Table 5, it is obvious that the occupants’ performance

is greatly affected by the change of the value ofthe variable X2, X4 (Time of the day, and Glare)range = 49.46% and range = 38.69% respectively,because these have the maximum range. At the otherextreme, the lighting factors, which are the variables“Energy Awareness Campaigns” X7, and “Orientation”X1, are range = 10.22% and range= 21.24% respectively.Based upon the sensitivity analysis of regression model,performance curves were developed in order to measurethe variation of the occupants’ performance versus thevarious lighting factors as shown in Tables 5 and 6.Figure 5 shows the curves of various lighting factors,

which are directly related to occupants’ performance,i.e., the more the lighting factor value, the more the

Table 2. Analysis of variance for the developed regression model

Factor X Predictor Coefficient P-valueConstant 53.48 0

1 Orientation -0.2124 0.0032 Time of Day -0.4946 03 Sky Condition -0.2315 0.0064 Glare 0.3869 05 Lighting location Control -0.2731 06 Word of Mouth 0.3038 07 Energy awareness campaigns 0.1022 0.018 Feedback techniques 0.2699 0.001

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Table 3. Best subset analysis

Vars

R-Sq

R-Sq(adj)

R-sq(pred)

S Cpvalue

Orientation

Tim

eof

day

Skycond

ition

Glare

Windo

wsize

towallr

atio

Glazing

color

Lightinglocation

control

Wordof

mou

th

Ene

rgyaw

aren

esscampa

igns

Finan

cial

incentives

Feed

back

techniqu

es

1 24 21.3 4.9 10.35 165.5 *1 15.1 12.1 0 10.94 187.9 *2 66.5 64 62 6.99 60.3 * *2 56 52.7 50.4 8.02 86.8 * *3 72.2 69 65.2 6.49 47.9 * * *3 72.1 68.9 65 6.5 48.2 * * *4 77.2 73.5 67.9 6 37.6 * * * *4 76.5 72.8 69.1 6.08 39.1 * * * *5 81.9 78.1 68.7 5.46 27.7 * * * * *5 81.3 77.4 73.1 5.54 29.1 * * * * *6 87.1 83.7 73.5 4.71 16.6 * * * * * *6 86.3 82.8 80.1 4.84 18.4 * * * * * *7 89.1 85.7 76.8 4.41 13.4 * * * * * * *7 89.1 85.6 80.2 4.43 13.6 * * * * * * *8 92.1 89.1 83.3 3.85 7.9 * * * * * * * *8 91.3 88 79.3 4.04 9.9 * * * * * * * *9 92.3 88.8 77.7 3.9 9.5 * * * * * * * * *9 92.3 88.8 81 3.9 9.5 * * * * * * * * *10 92.6 88.8 71.6 3.9 10.5 * * * * * * * * * *10 92.5 88.5 75.6 3.95 10.9 * * * * * * * * * *11 92.9 88.5 68.4 3.95 12 * * * * * * * * * * *

Table 4. Validation results of the developed model

Case No. Actual Performance (%) Predicted Performance (%) AIP1 60 57.33 0.042 40 44 0.13 40 38.9 0.034 40 40.61 0.025 40 38.99 0.036 20 23.34 0.177 40 42.66 0.07

AIP (%) = Σ 6.38AVP (%) 93.62

Table 5. Summary of sensitivity analysis results for the developed model

No. Lighting factor Regression model performance(%)Min. value Max. value Performance range

1 X1 Orientation 24.94 46.18 21.242 X2 Time of day 6.04 55.5 49.463 X3 Sky condition 23.9 47.05 23.154 X4 Glare 39.18 77.87 38.695 X5 Lighting location control 11.87 39.18 27.316 X6 Word of mouth 31.58 61.96 30.387 X7 Energy awareness campaigns 36.62 46.84 10.228 X8 Feedback techniques 32.43 59.42 26.99

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Table 6. Summary of sensitivity analysis results for the developed models

Environmental Physical Policies Performance

Orientation

Tim

eof

Day

SkyCon

dition

Glare

Lightinglocation

control

Wordof

Mou

th

Ene

rgyaw

aren

esscampa

igns

Feed

back

Techn

ique

s

Perform

ance

%

0 33 34 0 0 25 25 25 46.1820 33 34 0 0 25 25 25 41.9440 33 34 0 0 25 25 25 37.6960 33 34 0 0 25 25 25 33.4480 33 34 0 0 25 25 25 29.19100 33 34 0 0 25 25 25 24.9433 0 34 0 0 25 25 25 55.533 20 34 0 0 25 25 25 45.6133 40 34 0 0 25 25 25 35.7133 60 34 0 0 25 25 25 25.8233 80 34 0 0 25 25 25 15.9333 100 34 0 0 25 25 25 6.0433 33 0 0 0 25 25 25 47.0533 33 20 0 0 25 25 25 42.4233 33 40 0 0 25 25 25 37.7933 33 60 0 0 25 25 25 33.1633 33 80 0 0 25 25 25 28.5333 33 100 0 0 25 25 25 23.933 33 34 0 0 25 25 25 39.1833 33 34 20 0 25 25 25 46.9133 33 34 40 0 25 25 25 54.6533 33 34 60 0 25 25 25 62.3933 33 34 80 0 25 25 25 70.1333 33 34 100 0 25 25 25 77.8733 33 34 0 0 25 25 25 39.1833 33 34 0 20 25 25 25 33.7133 33 34 0 40 25 25 25 28.2533 33 34 0 60 25 25 25 22.7933 33 34 0 80 25 25 25 17.3333 33 34 0 100 25 25 25 11.8733 33 34 0 0 0 25 25 31.5833 33 34 0 0 20 25 25 37.6633 33 34 0 0 40 25 25 43.7333 33 34 0 0 60 25 25 49.8133 33 34 0 0 80 25 25 55.8833 33 34 0 0 100 25 25 61.9633 33 34 0 0 25 0 25 36.6233 33 34 0 0 25 20 25 38.6633 33 34 0 0 25 40 25 40.7133 33 34 0 0 25 60 25 42.7533 33 34 0 0 25 80 25 44.833 33 34 0 0 25 100 25 46.8433 33 34 0 0 25 25 0 32.4333 33 34 0 0 25 25 20 37.8333 33 34 0 0 25 25 40 43.2233 33 34 0 0 25 25 60 48.6233 33 34 0 0 25 25 80 54.0233 33 34 0 0 25 25 100 59.42

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Figure 5. Factors that are directly related to occupants’ performance (positive slope)

Figure 6. Factors that are inversely related to occupants’ performance (negative slope)

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occupants’ performance will be. Figure 6 shows thelighting factors, which are inversely related to theoccupants’ performance. The developed curves showthat occupants’ performance was directly related to“Glare”, i.e., the more Glare avoided, the better theperformance. The developed curves also show thatoccupants’ performance is directly related to “Word ofMouth”, “Energy awareness campaigns”, and “Feedbacktechniques”. It makes sense that to increase occupants’performance, “Word of Mouth”, “Energy AwarenessCampaigns”, can be combined with feedback techniquesto motivate people to save energy. On the contrary, theoccupants’ performance is inversely related to “WindowOrientation”, “Time of Day”, “Sky Condition”, and“Lighting Location Control”. It makes sense thatoccupants performance decreases according to the time ofthe day from morning to afternoon. Although the windoworientation, sky condition, and lighting location controlare related directly to the daylighting performance, thesefactors were loosely related to occupants’ performancein this study. Therefore, using the window orientation,or sky condition or lighting location control factorsmay not be appropriate for the evaluation of occupants’performance.

9 CONCLUSIONS AND FUTURERESEARCH

Lighting energy consumption has been identified asone of the high energy consumers in commercialbuildings. Achieving greater energy efficiency incommercial buildings demands considering the lightingpreferences of the users as well as the occupants’performance in respect to these lighting factors in theworkplace. This study has presented a methodology and amodel for institutional building occupant’s performance.A multi-dimensional study on the performance of theoccupants in the commercial buildings was conductedusing collected data from different intuitional buildings.This data was analyzed using a regression technique tobuild the model that predicts occupants’ performance.The model was validated with 93.62% Average ValidityPercent (AVP) and R-square predicted of 83.3. Thesewere considered satisfactory results.The future research should include a procedure to

decrease the number of factors and to collect more datasets. The developed model should prove beneficial toboth architects and practitioners and help them to choosethe appropriate workplace design, taking into account theoccupants’ preferences, to enhance both performance andenergy efficiency. The model will help decision makersand the designers at different levels to design workplacesthat meet the required levels of visual comfort for theusers, while also saving energy used in lighting. It alsoprovides energy modeling professionals with the variousessential factors that affect occupants’ performance andhow these can be assessed and predicted.

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