Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review

Dong, Bing; Liu, Yapan; Fontenot, Hannah; Ouf, Mohamed; Osman, Mohamed; Chong, Adrian; Qin, Shuxu; Salim, Flora D.; Xue, Hao; Yan, Da; Yuan, Jiao; Han, Mengjie; Zhang, Xingxing; Azar, Elie; Carlucci, Salvatore

doi:10.1016/j.apenergy.2021.116856

Cited by 51 publications

(11 citation statements)

References 169 publications

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“…Then, we can predict the population density distribution in an unknown time span and spatial range by learning and mastering the dependency laws of this relative steady state 18 . According to different basic theories and methods, there are four major methods existing for predicting urban spatial–temporal behaviours 19 – 21 .…”

Section: Critical Review Of Four Main Prediction Methodsmentioning

confidence: 99%

The spatiotemporal prediction method of urban population density distribution through behaviour environment interaction agent model

Yang

Shi

et al. 2023

Sci Rep

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Based on the interrelationship between the built environment and spatial–temporal distribution of population density, this paper proposes a method to predict the spatial–temporal distribution of urban population density using the depth residual network model (ResNet) of neural network. This study used the time-sharing data of mobile phone users provided by the China Mobile Communications Corporation to predict the time–space sequence of the steady-state distribution of population density. Firstly, 40 prediction databases were constructed according to the characteristics of built environment and the spatial–temporal distribution of population density. Thereafter, the depth residual model ResNet was used as the basic framework to construct the behaviour–environment agent model (BEM) for model training and prediction. Finally, the average percentage error index was used to evaluate the prediction results. The results revealed that the accuracy rate of prediction results reached 76.92% in the central urban area of the verification case. The proposed method can be applied to prevent urban public safety incidents and alleviate pandemics. Moreover, this method can be practically applied to enable the construction of a “smart city” for improving the efficient allocation of urban resources and traffic mobility.

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Section: Critical Review Of Four Main Prediction Methodsmentioning

confidence: 99%

The spatiotemporal prediction method of urban population density distribution through behaviour environment interaction agent model

Yang

Shi

et al. 2023

Sci Rep

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“…Occupant behavior (OB) modelling has been widely used on the building level, and it has become an important factor in the investigation of building energy consumption (Dong et al 2021;Tang et al 2021), building space and facility management (Stjelja et al 2020), as well building HVAC occupant-centric controls (Yang et al 2022). Majority of current studies have devoted to the quantification and modelling of occupant behavior within the building space (Dong et al 2018), for the purpose of understanding how many persons in the space, how people use a space and how their behavior impacts on a building's energy performance and indoor air quality, together with control of airborne infections risk nowadays (Carlucci et al 2020;Du et al 2020;Qian et al 2020;Wang et al 2022).…”

Section: Ob Modelling For Building-and District-scale Energy Simulationmentioning

confidence: 99%

Modelling method of inter-building movement for campus-scale occupancy simulation: A case study

Zhu

Pan

et al. 2022

Build. Simul.

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As an important factor in the investigation of building energy consumption, occupant behavior (OB) has been widely studied on the building level. However so far, studies of OB modelling on the district scale remain limited. Indeed, district-scale OB modelling has been facing the challenges from the scarcity of district-scale data, modelling methods, as well as simulation application. This study initiates the extrapolation of occupancy modelling methodology from building level to district scale through proposing modelling methods of inter-building movements. The proposed modelling methods utilize multiple distribution fittings and Bayesian network to upscale the event description methods from inter-zone movement events at the building level to inter-building movement events at the district level. This study provides a framework on the application of the proposed modelling methods for a university campus in the suburbs of Shanghai, taking advantages of data sensing, monitoring and survey techniques. With the collected campus-scale occupancy data, this paper defines five patterns of inter-building movement. One pattern represents the dominated inter-building movement events for one kind of students in their daily campus life. Based on the quantitative descriptions for various inter-building movement events, this study performs the stochastic simulation for the campus district, using Markov chain models.The simulation results are then validated with the campus-scale occupancy measurement data. Furthermore, the impact of inter-building movement modelling methods on building energy demand is evaluated for the library building, taking the deterministic occupancy schedules suggested by current building design standard as a baseline.

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“…The occupancy profiles of buildings play a crucial role in energy consumption ( Buttitta and Finn, 2020 , Motuzienė et al, 2022 ). Human behavior, different occupancy densities, and variations in thermal and lighting preferences contribute significantly to the gap between simulated and real energy performance in buildings ( Martinaitis, Zavadskas, Motuzienė and Vilutienė, 2015 , Dong et al, 2021 , Wu et al, 2020 ). However, the literature on defining occupancy scenarios during the COVID-19 pandemic is still scarce, and contrasting effects have been observed in different sectors and countries.…”

Section: Introductionmentioning

confidence: 99%

Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior

Todeschi

Javanroodi

Castello

et al. 2022

Sustainable Cities and Society

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Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review

Cited by 51 publications

References 169 publications

The spatiotemporal prediction method of urban population density distribution through behaviour environment interaction agent model

The spatiotemporal prediction method of urban population density distribution through behaviour environment interaction agent model

Modelling method of inter-building movement for campus-scale occupancy simulation: A case study

Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior

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