上海交通大学：公共管理类《专业英语》课程教学资源（阅读材料）A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices

Building and Environment 70(2013)31-47 Contents lists available at ScienceDirect Building and Environment ELSEVIER journal homepage:www.elsevier.com/locate/buildenv A critical review of observation studies,modeling,and simulation of CrossMark adaptive occupant behaviors in offices H.Burak Gunay,William O'Brien,Ian Beausoleil-Morrison b Carleton University,Department of Civil and Environmental Engineering.Canada Carleton Universiry.Department of Mechanical and Aerospace Engineering.Canada ARTICLE INFO ABSTRACT Article history: Occupants'behaviors account for significant uncertainty in building energy use.A better understanding Received 2 June 2013 of occupant behaviors is needed in order to manage this uncertainty;as such many studies have been Received in revised form dedicated to this topic.The current paper reviewed the research on adaptive occupant behaviors by 25July2013 sorting it into three categories.The first group encompasses all observational studies.The second group Accepted 31 July 2013 includes modeling studies.The third group incorporates the simulation studies.The current paper Keywords: presents the methodologies used in these studies,discusses the limitations associated with their application,and develops recommendations for future work.Generalized linear models-in particular Adaptive occupant behaviors Behavioral modeling logistic regression models-were found to be appropriate for modeling occupant behavior.Reversal of Occupant control of indoor environment adaptive behaviors (e.g.window closing)was modeled with deadband models or survival models Review Occupant models were typically simulated as discrete-time Markov processes.It was concluded that with appropriate selection of building geometry and materials and occupant-predicting control strategies. impact of occupant behaviors on the building performance can be reduced. 2013 Elsevier Ltd.All rights reserved. 1.Introduction affected if they have less control over their environment [8.12]. CIBSE [13]and ASHRAE [14]acknowledge this by including adap- Building Performance Simulation(BPS)based design,despite its tive comfort models for naturally ventilated buildings.Occupants potential for significant improvements in energy use and indoor can also adapt their personal characteristics such as adjusting their environment,has often been undermined by predictions that do typical beverage temperatures,location,posture,activity and not fully represent actual performance [1,2.Some of these dis- clothing levels.These personal adaptive behaviors can be restricted crepancies can be attributed to deviations from standard weather with social factors such as workplace dress codes however,even in data [3],modeling and simulation simplifications [4],occupancy the most sealed and fully conditioned buildings there are some profiles [5-7].unanticipated control behavior,and material/work- adaptive opportunities. manship related uncertainties.However,the uncertainty intro- Adaptive actions,aside from their impact on perceived comfort, duced by occupant behaviors are undeniable[8.9. often have significant impacts on energy use.Therefore,building Occupants adapt their environment and personal characteristics designers should foresee these occupant-use related impacts on to achieve their comfort in ways that are convenient to them rather energy consumption and incorporate them into design.However. than being necessarily energy-conserving [2,10.11].Environmental building designers tend to make static assumptions about occupant adjustments may involve decisions such as window/door opening, behavior,whereas field studies have indicated that occupants may blind/shade positioning,light switch on/off,carpet/hardwood floor act in unexpected ways and respond to crises of discomfort [2.15] covering,fan on/off,and thermostat up/down.In a given building. For example,an occupant may add carpet or hardwood flooring on occupants may or may not be given control over these actions,but top of concrete in a passive solar house;failure to consider this it was reported that occupants'comfort perception is negatively action will lead to inaccurate BPS predictions16.A better un- derstanding of occupant behaviors(aside from being a promising way to test buildings with expected occupant actions during the Corresponding author.Carleton University,Department of Civil and Environ- mental Engineering.1125 Colonel by Drive.Ottawa,Ontario K1S 5B6.Canada. design stage)has been recently acknowledged as a promising way Tel:+16135202600x8037:fax:+16135203951. to operate buildings [16].Clarke et al.[16].Thrun [17],Claridge and E-mail address:Liam_OBrien@carleton.ca (W.O'Brien). Abushakra [18],Guillemin and Molteni [19,20]and Dong et al.[21] 0360-1323/$-see front matter 2013 Elsevier Ltd.All rights reserved. http://dx.doiorg/10.1016/j.buildenv.2013.07.020

A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices H. Burak Gunay a , William O’Brien a,*, Ian Beausoleil-Morrison b a Carleton University, Department of Civil and Environmental Engineering, Canada b Carleton University, Department of Mechanical and Aerospace Engineering, Canada article info Article history: Received 2 June 2013 Received in revised form 25 July 2013 Accepted 31 July 2013 Keywords: Adaptive occupant behaviors Behavioral modeling Occupant control of indoor environment Review abstract Occupants’ behaviors account for significant uncertainty in building energy use. A better understanding of occupant behaviors is needed in order to manage this uncertainty; as such many studies have been dedicated to this topic. The current paper reviewed the research on adaptive occupant behaviors by sorting it into three categories. The first group encompasses all observational studies. The second group includes modeling studies. The third group incorporates the simulation studies. The current paper presents the methodologies used in these studies, discusses the limitations associated with their application, and develops recommendations for future work. Generalized linear models e in particular, logistic regression models e were found to be appropriate for modeling occupant behavior. Reversal of adaptive behaviors (e.g. window closing) was modeled with deadband models or survival models. Occupant models were typically simulated as discrete-time Markov processes. It was concluded that with appropriate selection of building geometry and materials and occupant-predicting control strategies, impact of occupant behaviors on the building performance can be reduced.
2013 Elsevier Ltd. All rights reserved. 1. Introduction Building Performance Simulation (BPS) based design, despite its potential for significant improvements in energy use and indoor environment, has often been undermined by predictions that do not fully represent actual performance [1,2]. Some of these dis￾crepancies can be attributed to deviations from standard weather data [3], modeling and simulation simplifications [4], occupancy profiles [5e7], unanticipated control behavior, and material/work￾manship related uncertainties. However, the uncertainty intro￾duced by occupant behaviors are undeniable [8,9]. Occupants adapt their environment and personal characteristics to achieve their comfort in ways that are convenient to them rather than being necessarily energy-conserving [2,10,11]. Environmental adjustments may involve decisions such as window/door opening, blind/shade positioning, light switch on/off, carpet/hardwood floor covering, fan on/off, and thermostat up/down. In a given building, occupants may or may not be given control over these actions, but it was reported that occupants’ comfort perception is negatively affected if they have less control over their environment [8,12]. CIBSE [13] and ASHRAE [14] acknowledge this by including adap￾tive comfort models for naturally ventilated buildings. Occupants can also adapt their personal characteristics such as adjusting their typical beverage temperatures, location, posture, activity and clothing levels. These personal adaptive behaviors can be restricted with social factors such as workplace dress codes however, even in the most sealed and fully conditioned buildings there are some adaptive opportunities. Adaptive actions, aside from their impact on perceived comfort, often have significant impacts on energy use. Therefore, building designers should foresee these occupant-use related impacts on energy consumption and incorporate them into design. However, building designers tend to make static assumptions about occupant behavior, whereas field studies have indicated that occupants may act in unexpected ways and respond to crises of discomfort [2,15]. For example, an occupant may add carpet or hardwood flooring on top of concrete in a passive solar house; failure to consider this action will lead to inaccurate BPS predictions [16]. A better un￾derstanding of occupant behaviors (aside from being a promising way to test buildings with expected occupant actions during the design stage) has been recently acknowledged as a promising way to operate buildings [16]. Clarke et al. [16], Thrun [17], Claridge and Abushakra [18], Guillemin and Molteni [19,20] and Dong et al. [21] * Corresponding author. Carleton University, Department of Civil and Environ￾mental Engineering, 1125 Colonel by Drive, Ottawa, Ontario K1S 5B6, Canada. Tel.: þ1 613 520 2600x8037; fax: þ1 613 520 3951. E-mail address: Liam_OBrien@carleton.ca (W. O’Brien). Contents lists available at ScienceDirect Building and Environment journal homepage: www.elsevier.com/locate/buildenv 0360-1323/$ e see front matter
2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.buildenv.2013.07.020 Building and Environment 70 (2013) 31e47

2 H.B.Gunay et aL Building and Environment 70(2013)31-47 have been pioneering this approach to retrieve occupancy-related simulation studies.In these studies,occupant behavior models information using inverse models which later can be utilized to were simulated (e.g.discrete-time Markov Chains)with the create intelligent(i.e.learning.predicting,and adapting)control building energy models to predict the energy impacts of occupants strategies. behaviors for adapting building design and control.The current The existing scientific literature on which these pioneering paper presents the methodologies used in these studies,discusses research efforts were based covers a broad range of methodologies the limitations associated with their application,and develops to study adaptive occupant behaviors.However,existing review recommendations for future work.Due to substantial contextual papers on occupant behaviors give high resolution insights into differences,occupant behaviors in residential buildings,although particular adaptive behaviors such as only manual control of win- they account for about the same amount of energy use [27].were dows [22,23].window shading devices [24,25]or lighting [20.26] not included in this paper.These contextual differences can be with an emphasis on the observational methodologies and their explained with the responsibility of energy bills,need for privacy. limitations.In this paper,a comprehensive,yet broad,approach social factors,type of activities/task,et cetera.The long-term was taken to cover common findings and limitations of the occu- objective of this research project is to develop building design pant behavior research in general with an equal emphasis on the and operation strategies which better account for occupants'be- observational,modeling,and simulation methodologies haviors,habits,and preferences The current paper reviewed the research on adaptive occupant behaviors in offices by sorting it into three categories as shown in 2.System observation Fig.1.These categories were formed to represent the logical flow of research approach for any phenomena:observe-model To assess the adaptive actions of occupants,researchers have simulate.This will help revealing the research needed from each observed a system to be able to correlate a state(e.g.window po- category.The first group encompasses all observational studies.In sition)with a set of variables (e.g.indoor air temperature).The these studies,researchers observed a system (e.g.naturally venti- validity of extending the conclusions of these observations to lated office building)for a period of time(e.g.heating season)in another context may be restricted to the characteristics of the order to develop a correlation between the observed state (e.g. observed building envelope and operation [28.Moreover,tech- operable window or window shades)and the monitored variables niques employed to collect information about the adaptive be- (e.g.indoor temperature).The second group includes modeling haviors (e.g.time-lapse photography,sensors)and the monitored studies.In these studies,occupant behavior models were predicted physical (e.g.indoor/outdoor thermal and non-thermal)and non- by assuming an idealized probability distribution(e.g.binomial)via physical (e.g.privacy,view to outside)variables constitute limita- a regression analysis(e.g.logistic)to reveal the predictor variables tions for the future models proposed based on these observations. that drive an adaptive behavior.The third group incorporates the This section identifies the factors that may affect the generality of Adaptive Occupant Behavior State Monitoring State discretization method (e.g.open/closed or open/half-open/closed) System Observation State monitoring method Overview of system (e.g.photography or sensory) (e.g.south-facing office building) State monitoring frequency Size of system (e.g.two per day) (e.g.300 offices) Observation period Variable Monitoring (e.g.heating season) Monitored variables (e.g.workplane illuminance,temperature) Model Prediction Adaptive behavior model type (e.g.logistic regression) Model validation method (e.g.cross-validation) Reversal of adaptive behavior model (e.g.survival models) Simulation Model simulation method (e.g.discrete time Markov Chains) Simulation verification method (e.g.isolate tests) Fig.1.Research and modeling approach on adaptive occupant behavior

have been pioneering this approach to retrieve occupancy-related information using inverse models which later can be utilized to create intelligent (i.e. learning, predicting, and adapting) control strategies. The existing scientific literature on which these pioneering research efforts were based covers a broad range of methodologies to study adaptive occupant behaviors. However, existing review papers on occupant behaviors give high resolution insights into particular adaptive behaviors such as only manual control of win￾dows [22,23], window shading devices [24,25] or lighting [20,26] with an emphasis on the observational methodologies and their limitations. In this paper, a comprehensive, yet broad, approach was taken to cover common findings and limitations of the occu￾pant behavior research in general with an equal emphasis on the observational, modeling, and simulation methodologies. The current paper reviewed the research on adaptive occupant behaviors in offices by sorting it into three categories as shown in Fig. 1. These categories were formed to represent the logical flow of research approach for any phenomena: observe / model / simulate. This will help revealing the research needed from each category. The first group encompasses all observational studies. In these studies, researchers observed a system (e.g. naturally venti￾lated office building) for a period of time (e.g. heating season) in order to develop a correlation between the observed state (e.g. operable window or window shades) and the monitored variables (e.g. indoor temperature). The second group includes modeling studies. In these studies, occupant behavior models were predicted by assuming an idealized probability distribution (e.g. binomial) via a regression analysis (e.g. logistic) to reveal the predictor variables that drive an adaptive behavior. The third group incorporates the simulation studies. In these studies, occupant behavior models were simulated (e.g. discrete-time Markov Chains) with the building energy models to predict the energy impacts of occupants’ behaviors for adapting building design and control. The current paper presents the methodologies used in these studies, discusses the limitations associated with their application, and develops recommendations for future work. Due to substantial contextual differences, occupant behaviors in residential buildings, although they account for about the same amount of energy use [27], were not included in this paper. These contextual differences can be explained with the responsibility of energy bills, need for privacy, social factors, type of activities/task, et cetera. The long-term objective of this research project is to develop building design and operation strategies which better account for occupants’ be￾haviors, habits, and preferences. 2. System observation To assess the adaptive actions of occupants, researchers have observed a system to be able to correlate a state (e.g. window po￾sition) with a set of variables (e.g. indoor air temperature). The validity of extending the conclusions of these observations to another context may be restricted to the characteristics of the observed building envelope and operation [28]. Moreover, tech￾niques employed to collect information about the adaptive be￾haviors (e.g. time-lapse photography, sensors) and the monitored physical (e.g. indoor/outdoor thermal and non-thermal) and non￾physical (e.g. privacy, view to outside) variables constitute limita￾tions for the future models proposed based on these observations. This section identifies the factors that may affect the generality of Fig. 1. Research and modeling approach on adaptive occupant behavior. 32 H.B. Gunay et al. / Building and Environment 70 (2013) 31e47

H.B.Gunay et al.Building and Environment 70 (2013)31-47 33 the observations and develops recommendations that will incor- parameters,despite their importance being qualitatively acknowl- porate the bias introduced by these factors. edged,have not been included in the reviewed literature.Haldi and Robinson [46]suggested building statistical models that incorpo- 2.1.Behaviors that adapt the indoor environment rate non-temperature physical variables as predictors as a future research effort.However,as window openings connect the ambient Occupants adapt their indoor environment with their alter- conditions to the indoor environment,the type of the window ations to operable windows and window shading devices,lights. opening can also play a significant role on the occupant's prefer- fans,carpets,and thermostats.Various research projects have ences.For example,bottom hung inside opening windows provide conducted observational studies where they investigated these weather protection[22.Thus,in this case,wind and rain may be adaptive behaviors using a variety of methods.This section pre- discarded from the monitored variables.But for the side hung or sents these methodologies and discusses the limitations and sliding windows,it may be more appropriate to include the associated challenges. weather variables to the monitored variables. Similarly,to predict a window shade deployment model,indoor 2.1.1.Physical variables variables,such as indoor temperature 29,32,47,49,indoor daylight Researchers have either used their prior knowledge on the 29,32,47,49,50],transmitted solar radiation [29,32,50,51]:and observed system or they carried out questionnaire surveys to nar- outdoor variables,such as outdoor temperature [39,47]and row down the variables that should be measured,such as tem- external solar radiation [49.50,52-54].have been monitored. perature,relative humidity(RH),noise,and workplane illuminance. Zhang and Barrett [47]observed that window shade deployment so that the adaptive occupant behaviors can be predicted with did not follow the outdoor temperature.Arguably.Lindsay and these monitored variables.For example,Inkarojrit [29]carried out a Littlefair [52]and Foster and Oreszczyn55]claimed that indoor survey to identify the main motivations for closing window blinds temperature and external solar radiation cannot be a predictor in private offices.In this study,it was reported that the majority of variable for the window shade deployment.Also,Reinhart [56 occupants who closed their blinds do so to protect their worksta- used only visual/optical variables that may lead to blind lowering. tions and screens from direct or reflected glare from sunlight,while A reasonable explanation for this controversy is that occupants use 27.4%of the participants claimed that they use their blinds to window shades to mitigate both visual and thermal discomfort reduce the heat from the sun and only 12.3%stated privacy and (excluding the non-physical factors such as view or privacy).which security as a reason for blind closure.Eilers et al.[30]surveyed can be caused by temperature,solar radiation,glare,et cetera. office occupants and confirmed that the majority of the subjects Clearly,it is crucial to monitor independent variables that can who closed their blinds do so to reduce the glare on their computer represent the window shade deployment.Reviewed literature screen.Similarly.Warren and Parkins [31]carried out a survey in suggests that these variables are:the depth of penetration of the which occupants stated that lAQ was the main reason for opening direct sunlight as a function of the solar altitude [47,51,57]and glare windows during the heating season and noise was the main reason 52,58,59].Inoue et al.[51]suggested that the depth of penetration for closing windows during the summer season.These surveys, of direct sunlight changes with the mean blind occlusion.This when used prior to the physical measurements,can give pre- observation was later confirmed by Reinhart and Voss [57]with a liminary insight into determining the variables that should be theoretical solar penetration depth measured from the top of the measured and the spatial distribution of sensors that will be placed window.Subsequently,Reinhart and Wienold 60]performed in the office during a study.Furthermore,it can be used to identify representative daylight design strategies that incorporate occu- the subtleties that are difficult to measure (e.g.rattling blinds pants'reaction against direct sunlight and glare. caused by wind passing over deployed venetian blinds)before starting the data collection 32]. 2.1.2.Non-physical parameters Early studies on the window opening behavior started by Physical variables (i.e.thermal,visual,acoustic,indoor air monitoring the outdoor variables [31.33-36]with the following environment)influence the chance that an office occupant will reasoning:(1)once these observations are integrated to BPS as experience discomfort.However,it is his/her social,economic,and occupant models the indoor variables become outputs of the BPS, psychological influences,which are driven by non-physical (i.e. therefore,the indoor variables cannot be more reliable than out- latent)variables that lead to these adaptive actions [61.These non- door variables [37.38];and (2)indoor temperature can be defined physical (latent)variables are parameters that are not measurable as a spatial distribution rather than a single scalar [38]which re- with typical sensors such as view and connection to the outside. quires time consuming and expensive instrumentation of the privacy or daylight-health perception. sensors and data loggers.Subsequently,it was suggested that oc- It is evident that one of the main design purposes of windows is cupants only have an indirect perception of outdoor physical vari- to provide a clear view and physical connection to the outside 60. ables [38].consequently indoor variables (at least the indoor Green building rating systems (e.g.LEED)define a view as "a thermal variables)have been incorporated amongst monitored straight visual connection from an interior point to a point outside variables in many of the recent studies [10,38-41.In particular, through a facade opening located within a certain height range within using a reasonable balance between the indoor and outdoor vari- a facade"[62].Inoue et al.[51]reported that most occupants ables to describe the window opening behavior can be suggested as preferred to have seats close to the windows,however these seats follows:(1)window opening behavior can be described with the were known to be the most susceptible locations to glare and solar indoor variables (e.g.indoor temperature);and (2)window closing radiation.Based on the surveys to study the stimulating factors for behavior can be explained with both indoor and outdoor variables window opening,it was reported that some of the window opening (e.g.indoor and outdoor temperature)[39,42].This would take into behaviors,despite allowing some ambient noise,may be explained account the transmitted effects of the outdoors once the window is to maintain a direct connection to outdoors [63,64].These findings open,while ignoring them once it is closed.Non-temperature can be interpreted as occupants prefer to tolerate some discomfort physical variables that can affect the window opening/closing in order to have a better quality of view and connection to the behavior have been listed as the indoor air quality [31,43-45.the outdoors. outdoor noise level 22.31,46,47],RH39].wind speed and direction Window shading devices may obstruct the view to the outside [22.34,47].and rain [41,47,48].These non-temperature physical Haldi and Robinson[65]carried out a study on window control

the observations and develops recommendations that will incor￾porate the bias introduced by these factors. 2.1. Behaviors that adapt the indoor environment Occupants adapt their indoor environment with their alter￾ations to operable windows and window shading devices, lights, fans, carpets, and thermostats. Various research projects have conducted observational studies where they investigated these adaptive behaviors using a variety of methods. This section pre￾sents these methodologies and discusses the limitations and associated challenges. 2.1.1. Physical variables Researchers have either used their prior knowledge on the observed system or they carried out questionnaire surveys to nar￾row down the variables that should be measured, such as tem￾perature, relative humidity (RH), noise, and workplane illuminance, so that the adaptive occupant behaviors can be predicted with these monitored variables. For example, Inkarojrit [29] carried out a survey to identify the main motivations for closing window blinds in private offices. In this study, it was reported that the majority of occupants who closed their blinds do so to protect their worksta￾tions and screens from direct or reflected glare from sunlight, while 27.4% of the participants claimed that they use their blinds to reduce the heat from the sun and only 12.3% stated privacy and security as a reason for blind closure. Eilers et al. [30] surveyed office occupants and confirmed that the majority of the subjects who closed their blinds do so to reduce the glare on their computer screen. Similarly, Warren and Parkins [31] carried out a survey in which occupants stated that IAQ was the main reason for opening windows during the heating season and noise was the main reason for closing windows during the summer season. These surveys, when used prior to the physical measurements, can give pre￾liminary insight into determining the variables that should be measured and the spatial distribution of sensors that will be placed in the office during a study. Furthermore, it can be used to identify the subtleties that are difficult to measure (e.g. rattling blinds caused by wind passing over deployed venetian blinds) before starting the data collection [32]. Early studies on the window opening behavior started by monitoring the outdoor variables [31,33e36] with the following reasoning: (1) once these observations are integrated to BPS as occupant models the indoor variables become outputs of the BPS, therefore, the indoor variables cannot be more reliable than out￾door variables [37,38]; and (2) indoor temperature can be defined as a spatial distribution rather than a single scalar [38] which re￾quires time consuming and expensive instrumentation of the sensors and data loggers. Subsequently, it was suggested that oc￾cupants only have an indirect perception of outdoor physical vari￾ables [38], consequently indoor variables (at least the indoor thermal variables) have been incorporated amongst monitored variables in many of the recent studies [10,38e41]. In particular, using a reasonable balance between the indoor and outdoor vari￾ables to describe the window opening behavior can be suggested as follows: (1) window opening behavior can be described with the indoor variables (e.g. indoor temperature); and (2) window closing behavior can be explained with both indoor and outdoor variables (e.g. indoor and outdoor temperature) [39,42]. This would take into account the transmitted effects of the outdoors once the window is open, while ignoring them once it is closed. Non-temperature physical variables that can affect the window opening/closing behavior have been listed as the indoor air quality [31,43e45], the outdoor noise level [22,31,46,47], RH [39], wind speed and direction [22,34,47], and rain [41,47,48]. These non-temperature physical parameters, despite their importance being qualitatively acknowl￾edged, have not been included in the reviewed literature. Haldi and Robinson [46] suggested building statistical models that incorpo￾rate non-temperature physical variables as predictors as a future research effort. However, as window openings connect the ambient conditions to the indoor environment, the type of the window opening can also play a significant role on the occupant’s prefer￾ences. For example, bottom hung inside opening windows provide weather protection [22]. Thus, in this case, wind and rain may be discarded from the monitored variables. But for the side hung or sliding windows, it may be more appropriate to include the weather variables to the monitored variables. Similarly, to predict a window shade deployment model, indoor variables, such as indoor temperature [29,32,47,49], indoor daylight [29,32,47,49,50], transmitted solar radiation [29,32,50,51]; and outdoor variables, such as outdoor temperature [39,47] and external solar radiation [49,50,52e54], have been monitored. Zhang and Barrett [47] observed that window shade deployment did not follow the outdoor temperature. Arguably, Lindsay and Littlefair [52] and Foster and Oreszczyn [55] claimed that indoor temperature and external solar radiation cannot be a predictor variable for the window shade deployment. Also, Reinhart [56] used only visual/optical variables that may lead to blind lowering. A reasonable explanation for this controversy is that occupants use window shades to mitigate both visual and thermal discomfort (excluding the non-physical factors such as view or privacy), which can be caused by temperature, solar radiation, glare, et cetera. Clearly, it is crucial to monitor independent variables that can represent the window shade deployment. Reviewed literature suggests that these variables are: the depth of penetration of the direct sunlight as a function of the solar altitude [47,51,57] and glare [52,58,59]. Inoue et al. [51] suggested that the depth of penetration of direct sunlight changes with the mean blind occlusion. This observation was later confirmed by Reinhart and Voss [57] with a theoretical solar penetration depth measured from the top of the window. Subsequently, Reinhart and Wienold [60] performed representative daylight design strategies that incorporate occu￾pants’ reaction against direct sunlight and glare. 2.1.2. Non-physical parameters Physical variables (i.e. thermal, visual, acoustic, indoor air environment) influence the chance that an office occupant will experience discomfort. However, it is his/her social, economic, and psychological influences, which are driven by non-physical (i.e. latent) variables that lead to these adaptive actions [61]. These non￾physical (latent) variables are parameters that are not measurable with typical sensors such as view and connection to the outside, privacy or daylight-health perception. It is evident that one of the main design purposes of windows is to provide a clear view and physical connection to the outside [60]. Green building rating systems (e.g. LEED) define a view as “a straight visual connection from an interior point to a point outside through a facade opening located within a certain height range within a facade” [62]. Inoue et al. [51] reported that most occupants preferred to have seats close to the windows, however these seats were known to be the most susceptible locations to glare and solar radiation. Based on the surveys to study the stimulating factors for window opening, it was reported that some of the window opening behaviors, despite allowing some ambient noise, may be explained to maintain a direct connection to outdoors [63,64]. These findings can be interpreted as occupants prefer to tolerate some discomfort in order to have a better quality of view and connection to the outdoors. Window shading devices may obstruct the view to the outside. Haldi and Robinson [65] carried out a study on window control H.B. Gunay et al. / Building and Environment 70 (2013) 31e47 33

H.B.Gunay et aL Building and Environment 70 (2013)31-47 with separate upper and lower blinds and reported that upper openings were suppressed with the larger number of window blinds were slightly more frequently used.The upper blinds were openings during the cooling season.However,it failed to explain found to be fully drawn four times more than the lower blinds. the fact that up to 20%of the windows were left open during the However,the relationship between the view and window shade heating season.This shows that the validity of observations may be use was inconclusive due to variability introduced by the presence limited to a particular season.For example,windows may be of anidolic reflectors.Other researchers 32,47,51,65]have also opened for promoting ventilation during the heating season,while acknowledged the view to the outside as a possible predictor var- during the cooling season it may occur in order to achieve both iable,yet a conclusive finding has not been suggested mainly cooling and ventilation [31.33.34.54.This suggests that proposing because of the interferences from other variables.For example, a general window opening model that is valid for both the heating Rubin,et al.[54]stated that the view to the other office buildings and the cooling season may not be possible. can conflict with the preference to maintain a private indoor space. Similar observations were reported in the studies on window Inkarojrit[32]reported that occupants'desire to maintain privacy shades.Mahdavi et al.[49]carried out a survey on three office as a secondary reason for choosing the blind positions.About 12%of buildings,which revealed that the proportion of the mean shade participants stated that privacy and security concerns represent deployment is up to 30%higher during the cooling season than the one of the reasons why they deploy their window shades.More- heating season.This was explained with the relatively higher solar over,Foster and Oreszczyn [55]unexpectedly observed higher radiation on the facade during cooling season.Even after sub mean blind occlusion rates in the north facade than the west stantial changes took place in the solar radiation and illuminance, facade.This was attributed to the fact that north facade of the occupants usually did not react to change the shade position building was facing another office building,which in turn may be [47.72].Window shades were rarely observed to be operated more explained with the efforts of occupants to preserve their privacy. than once a day [53,54]and even then,Bordass,et al.[15]reported Similarly,Reinhart and Voss57]aimed to correct the bias in their that window shades were typically set to mitigate the worst-case observations due to the privacy concerns and suggested that if condition.Thus,Zhang and Barrett [47]stated that window shade blinds were lowered at ambient horizontal illuminance less than position was based on occupants'long term perception and expe- 1000 lux,it would have occurred due to occupants'desire to rience rather than an instantaneous reaction against a particular maintain privacy.Therefore,a major task for BPS users that incor- stimulus.On the contrary,Haldi and Robinson 65 reported that porate occupant behavior into their studies is to predict the bias seasonal effects depend on other independent variables such as introduced by these non-physical variables and adapt the model indoor temperature or daylight level,thus were found statistically accordingly. insignificant.These results suggest that window shade deploy- Heerwagen and Heerwagen [63]carried out a survey on office ment,unlike window opening,may be used to develop a single occupants in a heating and cooling season and revealed that oc- model that is valid for both the cooling and the heating seasons. cupants widely believe daylight is crucial for their general health Begemann et al.[73]suggested that occupant light switch-on and essential for their work environment.Veitch et al.66 preferences were based on the desire to balance the variation be- confirmed that people believe daylight is superior to artificial tween the window brightness and the interior surfaces.Therefore, lighting for health.Participants reported that the quality of light it is expected that on a sunny summer day occupants tend to switch sources is crucial for their well-being and the florescent lighting on their lights to mitigate the large daylight gradients.This expla- can cause headaches and eyestrain [67.Therefore,the occupants nation can justify the lack of seasonal light energy usage variation preference to sit close to the windows can be explained with their in the UK,by inferring that the occupants can tolerate lower health concerns related to the artificial lighting along with benefit workplane illuminances during winter when the daylight level is of view and connection to the outside. lower and expect higher workplane illuminances during summer Visibility of energy use,which can be influenced with the when the daylight level is higher [741.Therefore,the workplane availability of various feedback sources,affects the behavioral illuminance should not be used alone as a predictor variable to adaptation of occupants[68.These direct and indirect feedbacks model the light switch-on behavior and it may be appropriate to may emerge from simple and more intuitive energy use dashboards incorporate window illuminance as a secondary predictor variable. [69].utility bills [70].competitions or awards [70].Darby [71] estimated that savings of up to ten percent can be achieved 2.1.4.Facade orientation through various feedback strategies,which suggests that occupants Facade orientation affects the magnitude and temporal distri- adapt their behaviors to save energy.In other words,the likelihood bution of the solar gains.For example,for the Northern Hemi- of undertaking a manual control action (e.g.turning off the lights sphere,the north facades receive the least solar gains,while south before departure)can be influenced with the visibility of energy facades receive the most useful solar radiation during the winter. use. Also,the solar penetration varies daily in zones adjacent to the east and west facades,but it varies more seasonally in the south zones 2.1.3.Seasonal effects As a result,naturally ventilated south facing offices tend to have Long-term observational studies revealed noticeable variations higher indoor temperatures than the east,west,and north facing in occupant adaptive behaviors between cooling and heating sea- offices [38].In line with this,the likelihood of opening windows in sons.For example,Fritsch,et al.[36]carried out an observational the south facade was observed to be 30%higher than the north study on the occupant control of windows in four offices in a facade [38].Zhang and Barrett [75]reported that the mean pro- heating season and a cooling season.In the heating season,the portion of windows open was 7.3%in the south facade:6.3%,5.6% window opening behavior was found to not follow the outdoor and 3.6%in the east,west,and north facades,respectively.This temperature,while during the cooling season the outdoor tem- implies that window opening behavior in east and west facades perature was a major factor leading to window opening.Similarly, follows a trend more similar to the south facade than the north Rijal,et al.[10]carried out a long term observational study to facade.Moreover,the peak percentage of window opening predict a window opening model,which was based on the aggre- times shifted following the peak solar radiation rather than the gated observations for cooling and heating seasons.The model was indoor temperature [38.This may be explained with the discom- in agreement with the cooling season observations of Fritsch et al. fort due to the transmitted solar radiation incident on the work- 36;perhaps the lower number of heating season window station and occupant.Similarly,mean window shade occlusion was

with separate upper and lower blinds and reported that upper blinds were slightly more frequently used. The upper blinds were found to be fully drawn four times more than the lower blinds. However, the relationship between the view and window shade use was inconclusive due to variability introduced by the presence of anidolic reflectors. Other researchers [32,47,51,65] have also acknowledged the view to the outside as a possible predictor var￾iable, yet a conclusive finding has not been suggested mainly because of the interferences from other variables. For example, Rubin, et al. [54] stated that the view to the other office buildings can conflict with the preference to maintain a private indoor space. Inkarojrit [32] reported that occupants’ desire to maintain privacy as a secondary reason for choosing the blind positions. About 12% of participants stated that privacy and security concerns represent one of the reasons why they deploy their window shades. More￾over, Foster and Oreszczyn [55] unexpectedly observed higher mean blind occlusion rates in the north facade than the west facade. This was attributed to the fact that north facade of the building was facing another office building, which in turn may be explained with the efforts of occupants to preserve their privacy. Similarly, Reinhart and Voss [57] aimed to correct the bias in their observations due to the privacy concerns and suggested that if blinds were lowered at ambient horizontal illuminance less than 1000 lux, it would have occurred due to occupants’ desire to maintain privacy. Therefore, a major task for BPS users that incor￾porate occupant behavior into their studies is to predict the bias introduced by these non-physical variables and adapt the model accordingly. Heerwagen and Heerwagen [63] carried out a survey on office occupants in a heating and cooling season and revealed that oc￾cupants widely believe daylight is crucial for their general health and essential for their work environment. Veitch et al. [66] confirmed that people believe daylight is superior to artificial lighting for health. Participants reported that the quality of light sources is crucial for their well-being and the florescent lighting can cause headaches and eyestrain [67]. Therefore, the occupants’ preference to sit close to the windows can be explained with their health concerns related to the artificial lighting along with benefit of view and connection to the outside. Visibility of energy use, which can be influenced with the availability of various feedback sources, affects the behavioral adaptation of occupants [68]. These direct and indirect feedbacks may emerge from simple and more intuitive energy use dashboards [69], utility bills [70], competitions or awards [70]. Darby [71] estimated that savings of up to ten percent can be achieved through various feedback strategies, which suggests that occupants adapt their behaviors to save energy. In other words, the likelihood of undertaking a manual control action (e.g. turning off the lights before departure) can be influenced with the visibility of energy use. 2.1.3. Seasonal effects Long-term observational studies revealed noticeable variations in occupant adaptive behaviors between cooling and heating sea￾sons. For example, Fritsch, et al. [36] carried out an observational study on the occupant control of windows in four offices in a heating season and a cooling season. In the heating season, the window opening behavior was found to not follow the outdoor temperature, while during the cooling season the outdoor tem￾perature was a major factor leading to window opening. Similarly, Rijal, et al. [10] carried out a long term observational study to predict a window opening model, which was based on the aggre￾gated observations for cooling and heating seasons. The model was in agreement with the cooling season observations of Fritsch et al. [36]; perhaps the lower number of heating season window openings were suppressed with the larger number of window openings during the cooling season. However, it failed to explain the fact that up to 20% of the windows were left open during the heating season. This shows that the validity of observations may be limited to a particular season. For example, windows may be opened for promoting ventilation during the heating season, while during the cooling season it may occur in order to achieve both cooling and ventilation [31,33,34,54]. This suggests that proposing a general window opening model that is valid for both the heating and the cooling season may not be possible. Similar observations were reported in the studies on window shades. Mahdavi et al. [49] carried out a survey on three office buildings, which revealed that the proportion of the mean shade deployment is up to 30% higher during the cooling season than the heating season. This was explained with the relatively higher solar radiation on the facade during cooling season. Even after sub￾stantial changes took place in the solar radiation and illuminance, occupants usually did not react to change the shade position [47,72]. Window shades were rarely observed to be operated more than once a day [53,54] and even then, Bordass, et al. [15] reported that window shades were typically set to mitigate the worst-case condition. Thus, Zhang and Barrett [47] stated that window shade position was based on occupants’ long term perception and expe￾rience rather than an instantaneous reaction against a particular stimulus. On the contrary, Haldi and Robinson [65] reported that seasonal effects depend on other independent variables such as indoor temperature or daylight level, thus were found statistically insignificant. These results suggest that window shade deploy￾ment, unlike window opening, may be used to develop a single model that is valid for both the cooling and the heating seasons. Begemann et al. [73] suggested that occupant light switch-on preferences were based on the desire to balance the variation be￾tween the window brightness and the interior surfaces. Therefore, it is expected that on a sunny summer day occupants tend to switch on their lights to mitigate the large daylight gradients. This expla￾nation can justify the lack of seasonal light energy usage variation in the UK, by inferring that the occupants can tolerate lower workplane illuminances during winter when the daylight level is lower and expect higher workplane illuminances during summer when the daylight level is higher [74]. Therefore, the workplane illuminance should not be used alone as a predictor variable to model the light switch-on behavior and it may be appropriate to incorporate window illuminance as a secondary predictor variable. 2.1.4. Facade orientation Facade orientation affects the magnitude and temporal distri￾bution of the solar gains. For example, for the Northern Hemi￾sphere, the north facades receive the least solar gains, while south facades receive the most useful solar radiation during the winter. Also, the solar penetration varies daily in zones adjacent to the east and west facades, but it varies more seasonally in the south zones. As a result, naturally ventilated south facing offices tend to have higher indoor temperatures than the east, west, and north facing offices [38]. In line with this, the likelihood of opening windows in the south facade was observed to be 30% higher than the north facade [38]. Zhang and Barrett [75] reported that the mean pro￾portion of windows open was 7.3% in the south facade; 6.3%, 5.6%, and 3.6% in the east, west, and north facades, respectively. This implies that window opening behavior in east and west facades follows a trend more similar to the south facade than the north facade. Moreover, the peak percentage of window opening times shifted following the peak solar radiation rather than the indoor temperature [38]. This may be explained with the discom￾fort due to the transmitted solar radiation incident on the work￾station and occupant. Similarly, mean window shade occlusion was 34 H.B. Gunay et al. / Building and Environment 70 (2013) 31e47

H.B.Gunay et al.Building and Environment 70(2013)31-47 子 reported lowest on north facades and highest on south facades that the number of monitored blind deployments during arrival [29,30,49,54.55].Shades on the north facade were rarely observed was 5.5 times more than that was during presence.Another as fully closed [30.76].Rea [53]and Zhang and Barrett [47]reported explanation suggests that on a sunny day the likelihood of turning that the mean shade occlusion in the east and west facades were the lights on and/or lowering the blinds increase,because the between that of the north facade and the south facade,however occupant wants to balance the brightness of the window areas with they were closer to that of the south facade.Given that the east and those of the interior 741.The increased probability of adaptive west facades are known to have greatest solar penetration depth measures upon arrival could also indicate that the adaptive mea- during the occupied hours and the south perimeter zones often sures taken for the previous occupancy period are no longer have the highest temperatures,the relative importance of the appropriate for current conditions.It is also worth noting that the temperature and the beam solar radiation may be discernible at convenience to undertake a certain adaptive behavior,once an different facade orientations.For example,to avoid frequent blind occupant is already standing upon arrival or prior to departure may use,occupants in the east and west facades can be more likely to increase the ease with which the adaptive action can be made [79]. leave their blinds fully closed.However,it has been suggested that However,no increase in the monitored blind deployment actions the effect of facade orientation itself can be treated as a dependant was observed during the occupant departure.It was concluded that variable of others such as temperature or beam solar radiation occupants do not adjust their blinds for predictive purposes during 24,46,65]:comparing adaptive behaviors in different facade ori- their absence [74].It should be noted that window opening/closing entations can give better insight into the relative significance of actions,unlike the shade deployment actions,were reported with a variables for future models. discernible frequency during departures [28].Likewise,the light switching was observed to take place during arrival and departure 2.1.5.HVAC system and operation 30,80,81].Switch-on actions during arrivals were frequently The type of HVAC system and operation may affect the adaptive explained by the daylight illuminances in the workplane [80.81]. occupant behaviors because occupants do not need to take as many. while the switch-off actions upon departure were explained with if any,adaptive measures if comfort conditions are automatically length of absence [561.Eilers et al.[30]showed that only about half provided.For example,occupants in a naturally ventilated building of the occupants switched off their lights if the departure was fol- may use their windows for different reasons than the occupants in lowed by an absence of two to four hours.Also,this ratio further a mechanically ventilated/cooled building.It was reported that if decreased once there were occupancy sensors or dimmed,indirect the indoor conditions were tightly controlled,occupants were lighting systems [30.57.It is also worth noting that not all these found less likely to undertake adaptive behaviors[24].For example, occupant behaviors aim at adapting their environment or adapting Rijal,et al.10]investigated the effect of open windows on thermal to their environment;instead,they can be habitual actions.For comfort and energy use in two mixed-mode buildings(i.e.build- example,occupants'action to turn on lights upon arrival,regardless ings with mechanical cooling with operable windows [77])and of brightness,can attest their arrival in a habitual manner [26]. seven naturally ventilated buildings.Occupants in the air- Therefore,not only the mere presence of the occupant,but also the conditioned buildings used their windows significantly less than state of presence (e.g.just arrived on a sunny day)should be occupants in the naturally ventilated buildings.Similarly,a study by incorporated in observational studies to be able to properly model Inkarojrit [32]revealed that the mean shade occlusion rate for the the adaptive occupant behavior. offices with air conditioning was 30%in comparison to the 49%for The number of occupants responsible for opening a particular those without.This can be interpreted that the validity of these window can also impact the overall window opening behavior observations should be restrained with the context of the moni- 46,781.Haldi and Robinson [46]observed a slight variation in the tored building or similar buildings window opening behavior in offices with one or two occupants. This was confirmed by similar observations by Herkel et al.[78]in 2.1.6.Occupancy pattern two or three person offices while studying manual blinds control Occupants'distance to the controlled device,time after their and by Moore et al.[74]in one to nine person offices while studying arrival or to their departure,or the number of occupants sharing light switching.However,in this case the aforementioned arrival the same controlled device may affect the likelihood of an adaptive and departure time intervals require further explanation.For behavior.Number of adaptive occupant behaviors per unit time was example,an occupant may walk into an already occupied office and found notably higher just after arrival and just before departure.For open the window.Considering this action as an intermediate example,Pfafferott and Herkel [40]suggested that window open- window opening behavior may mislead the model prediction ing is usually employed during arrival and departure.In between process.Haldi and Robinson [46]suggested a simplifying assump- the arrival and the departure,occupants were found less likely to tion that all occupants act independently and adaptive behavior is open or close a window.The arrival interval was defined as a time controlled by the most active (ie.occupant who uses the adaptive interval that was followed by the arrival and the departure interval control actions more frequently)occupant.Future studies may seek was defined as a time interval that was preceded by the departure for a justification for this assumption.On the contrary,occupants Haldi and Robinson [46]confirmed this observation and claimed tend to be more reluctant to use their blinds if others are present that the first five minutes after arrival and the last five minutes because of social constraints [24].It was reported that such control before the departure define a threshold limit for these arrival and actions in large offices were more frequently performed once departure intervals.Herkel et al.[78]suggested that arrival and most people had left because the action was judged to not impact departure time interval to be 15 min.Similar observations were anyone [82]. reported in the reviewed studies on the window shades and light The indoor conditions are a temporospatial distribution of a switching.This distinct behavior during arrival and departure was physical variable.For example,the illuminance even in a small of- explained with the occupant's preference to minimize the variation fice can vary significantly.In fact,Reinhart and Wienold [60 between the indoors and outdoors [73.This explanation suggests considered repositioning in the office as an independent adaptive that if an occupant walks in a dark office on a sunny day,he/she measure.Subsequently,Jakubiec and Reinhart [83]introduced likely switches the lights on upon arrival.This explanation can be adaptive zone concept in which occupants change position and refuted as the likelihood of blind deployment increases upon arrival view directions,rather than readily accepting the discomfort from in a sunny day as well.For example,Haldi and Robinson [65]stated glare or closing the blinds.It was reported that in a side-lit office

reported lowest on north facades and highest on south facades [29,30,49,54,55]. Shades on the north facade were rarely observed as fully closed [30,76]. Rea [53] and Zhang and Barrett [47] reported that the mean shade occlusion in the east and west facades were between that of the north facade and the south facade, however they were closer to that of the south facade. Given that the east and west facades are known to have greatest solar penetration depth during the occupied hours and the south perimeter zones often have the highest temperatures, the relative importance of the temperature and the beam solar radiation may be discernible at different facade orientations. For example, to avoid frequent blind use, occupants in the east and west facades can be more likely to leave their blinds fully closed. However, it has been suggested that the effect of facade orientation itself can be treated as a dependant variable of others such as temperature or beam solar radiation [24,46,65]; comparing adaptive behaviors in different facade ori￾entations can give better insight into the relative significance of variables for future models. 2.1.5. HVAC system and operation The type of HVAC system and operation may affect the adaptive occupant behaviors because occupants do not need to take as many, if any, adaptive measures if comfort conditions are automatically provided. For example, occupants in a naturally ventilated building may use their windows for different reasons than the occupants in a mechanically ventilated/cooled building. It was reported that if the indoor conditions were tightly controlled, occupants were found less likely to undertake adaptive behaviors [24]. For example, Rijal, et al. [10] investigated the effect of open windows on thermal comfort and energy use in two mixed-mode buildings (i.e. build￾ings with mechanical cooling with operable windows [77]) and seven naturally ventilated buildings. Occupants in the air￾conditioned buildings used their windows significantly less than occupants in the naturally ventilated buildings. Similarly, a study by Inkarojrit [32] revealed that the mean shade occlusion rate for the offices with air conditioning was 30% in comparison to the 49% for those without. This can be interpreted that the validity of these observations should be restrained with the context of the moni￾tored building or similar buildings. 2.1.6. Occupancy pattern Occupants’ distance to the controlled device, time after their arrival or to their departure, or the number of occupants sharing the same controlled device may affect the likelihood of an adaptive behavior. Number of adaptive occupant behaviors per unit time was found notably higher just after arrival and just before departure. For example, Pfafferott and Herkel [40] suggested that window open￾ing is usually employed during arrival and departure. In between the arrival and the departure, occupants were found less likely to open or close a window. The arrival interval was defined as a time interval that was followed by the arrival and the departure interval was defined as a time interval that was preceded by the departure. Haldi and Robinson [46] confirmed this observation and claimed that the first five minutes after arrival and the last five minutes before the departure define a threshold limit for these arrival and departure intervals. Herkel et al. [78] suggested that arrival and departure time interval to be 15 min. Similar observations were reported in the reviewed studies on the window shades and light switching. This distinct behavior during arrival and departure was explained with the occupant’s preference to minimize the variation between the indoors and outdoors [73]. This explanation suggests that if an occupant walks in a dark office on a sunny day, he/she likely switches the lights on upon arrival. This explanation can be refuted as the likelihood of blind deployment increases upon arrival in a sunny day as well. For example, Haldi and Robinson [65] stated that the number of monitored blind deployments during arrival was 5.5 times more than that was during presence. Another explanation suggests that on a sunny day the likelihood of turning the lights on and/or lowering the blinds increase, because the occupant wants to balance the brightness of the window areas with those of the interior [74]. The increased probability of adaptive measures upon arrival could also indicate that the adaptive mea￾sures taken for the previous occupancy period are no longer appropriate for current conditions. It is also worth noting that the convenience to undertake a certain adaptive behavior, once an occupant is already standing upon arrival or prior to departure may increase the ease with which the adaptive action can be made [79]. However, no increase in the monitored blind deployment actions was observed during the occupant departure. It was concluded that occupants do not adjust their blinds for predictive purposes during their absence [74]. It should be noted that window opening/closing actions, unlike the shade deployment actions, were reported with a discernible frequency during departures [28]. Likewise, the light switching was observed to take place during arrival and departure [30,80,81]. Switch-on actions during arrivals were frequently explained by the daylight illuminances in the workplane [80,81], while the switch-off actions upon departure were explained with length of absence [56]. Eilers et al. [30] showed that only about half of the occupants switched off their lights if the departure was fol￾lowed by an absence of two to four hours. Also, this ratio further decreased once there were occupancy sensors or dimmed, indirect lighting systems [30,57]. It is also worth noting that not all these occupant behaviors aim at adapting their environment or adapting to their environment; instead, they can be habitual actions. For example, occupants’ action to turn on lights upon arrival, regardless of brightness, can attest their arrival in a habitual manner [26]. Therefore, not only the mere presence of the occupant, but also the state of presence (e.g. just arrived on a sunny day) should be incorporated in observational studies to be able to properly model the adaptive occupant behavior. The number of occupants responsible for opening a particular window can also impact the overall window opening behavior [46,78]. Haldi and Robinson [46] observed a slight variation in the window opening behavior in offices with one or two occupants. This was confirmed by similar observations by Herkel et al. [78] in two or three person offices while studying manual blinds control and by Moore et al. [74] in one to nine person offices while studying light switching. However, in this case the aforementioned arrival and departure time intervals require further explanation. For example, an occupant may walk into an already occupied office and open the window. Considering this action as an intermediate window opening behavior may mislead the model prediction process. Haldi and Robinson [46] suggested a simplifying assump￾tion that all occupants act independently and adaptive behavior is controlled by the most active (i.e. occupant who uses the adaptive control actions more frequently) occupant. Future studies may seek for a justification for this assumption. On the contrary, occupants tend to be more reluctant to use their blinds if others are present because of social constraints [24]. It was reported that such control actions in large offices were more frequently performed once most people had left because the action was judged to not impact anyone [82]. The indoor conditions are a temporospatial distribution of a physical variable. For example, the illuminance even in a small of- fice can vary significantly. In fact, Reinhart and Wienold [60] considered repositioning in the office as an independent adaptive measure. Subsequently, Jakubiec and Reinhart [83] introduced adaptive zone concept in which occupants change position and view directions, rather than readily accepting the discomfort from glare or closing the blinds. It was reported that in a side-lit office H.B. Gunay et al. / Building and Environment 70 (2013) 31e47 35