Critical review and illustrative examples of office occupant modelling formalisms

Abstract

It is widely understood that occupants can have a significant impact on building performance. Accordingly, the field has benefited extensively from research efforts in the past decade. However, the methods and terminology involved in modelling occupants in buildings remains fragmented across a large number of studies. This fragmentation represents a major obstacle to those who intend to join in this research endeavor as well as for the convergence and standardization of methods. To address this issue, this paper investigates occupant modelling methods for the key domains of electric lighting, blinds, operable windows, thermostats, plug loads, and occupancy. In the reviewed literature, five broad categories of occupant model formalisms were identified: schedules, Bernoulli models, discrete-time Markov models, discrete-event Markov models, and survival models. Illustrative examples were provided from two independent datasets to demonstrate the strengths and weaknesses of these model forms. It was shown that Markov models are suitable to represent occupants' adaptive behaviors, while survival models are suitable to represent occupancy, non-adaptive behaviors, and infrequently executed adaptive behaviors, such as the blinds opening behavior. Practical application: The engineering application of the occupant modelling formalisms that are critically reviewed in this paper is that these models are highly beneficial for incorporating occupants' presence and behaviors into building design and control. Building design can be improved significantly regarding energy use and occupant comfort when the most suitable occupant models are implemented in simulation-aided building design process. Ultimately, like for any modelling domain, the most suitable model is dependent on the modelling objective (e.g. optimizing passive design, equipment sizing), building type and size, occupant-related domain (e.g. occupancy, window-opening behavior), and climate zones. Furthermore, there is great potential in improving occupant comfort and energy savings of existing buildings when occupants' presence and interactions with buildings' systems and components are predicted accurately using occupant models.

An international review of occupant-related aspects of building energy codes and standards

“…While much of the recent scientific literature is focused on stochastic occupant models (e.g., Chen, Hong et al 2018, D'Oca, Gunay et al 2019, we argue that they are not suitable for building energy code purposes -at least for the foreseeable future. Stochastic occupant models yield a different result every time a simulation is run, which causes complexity when performance paths of building codes rely on single simulations.…”

Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications

“…X Hong et al [29] Proposing the DNAs 'Drivers-Needs-Actions-Systems' framework providing an ontology to represent energy-related OB in buildings X X X Hong et al [30] Implementation of the DNAS framework proposed in [X] using an XML schema X X Østergård et al [32] Review of building simulations supporting decision making in the early design stage X Ouf et al [31] Review and comparison of occupant-related features between common BPS tools X Hong et al [17] Review of implementation and representation approaches of OB models in BPS programs X Lindner et al [33] Determination of requirements on occupant behavior models for the use in building performance simulations X X Gunay et al [40] Implementation and comparison of existing OB models in EnergyPlus X X O'Brien et al [35] Review, discussion, and guidance for developing and applying of occupant-centric building performance metrics X X Ouf et al [38] Proposing an approach and metrics to quantify building performance adaptability to variable occupancy X X X Machairas et al [22] Review of algorithms for optimization of building design X X Tian et al [15] Review and survey of building energy simulation and optimization applications to sustainable building design X X Kheiri et al [39] Review on optimization methods applied in energy-efficient building geometry and envelope design X X Shi et al [13] Review on building energy-efficient design optimization from the perspective of architects X Dong et al [11] Review on modeling occupancy and behavior for better building design and operation X D'Oca et al [26] and Zhang et al [27] reviewed and categorized the "human dimensions" of building performance and the need to integrate them into the operation and design processes. More specific reviews on various OB modeling approaches classified them into distinct formalisms [28], proposed a "fitfor-purpose" modeling strategy [11], or introduced an ontology to represent energy-related behaviors of building occupants [29,30]. Other papers focused on performing comparative reviews of occupant-related features and inputs in common BPS tools [31,32], or presented different approaches to implement OB models in BPS tools (e.g., [17,33,34]).…”

“…Numerous research efforts confirm the significant impact of indoor environmental conditions on the comfort, wellbeing, health, and productivity of occupants. Commonly-studied indoor environmental metrics include temperature, humidity, lighting, noise, and air quality levels [2][3][4][5][6].…”

Development of an Occupant-centric Control Algorithm for Mixed-Mode Ventilation Buildings to Regulate Window Operations

“…Many studies have developed various modelling formalisms such as Bernoulli, discrete-time Markov, discrete-event Markov, and survival models. Their strengths and weaknesses were demonstrated by a review [43], which suggest that the Markov models (i.e., discrete-time and discrete-event models) had the best predictive outcomes for occupants' adaptive behaviours (e.g., window opening actions and thermostat override actions), whereas the survival models are best suited to predict non-adaptive behaviours (e.g., light switch off actions). Moreover, occupant behaviour models often yield different accuracies even though environmental conditions are identical.…”

“…The significance of each predictor was assessed through a forward stepwise predictor selection approach. Note that this model form has been widely used in the occupant modelling literature [53], therefore the fundamentals are not repeated in the present study.…”