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 behaviorhave 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