Cluster analysis of occupancy schedules in residential buildings in the United States

Mitra, Debrudra; Chu, Yiyi; Cetin, Kristen

doi:10.1016/j.enbuild.2021.110791

Cited by 30 publications

(7 citation statements)

References 37 publications

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“…As shown in previous studies, building performance are highly dependent on the occupant behavior (Ren and Yan 2014;McKenna et al 2015;Jeong et al 2021;Mitra et al 2021). Thus, the proposed occupancy model can influence the predictions of other occupant behaviors by accurately predicting the occupancy, and then influence the building performance simulation indirectly.…”

Section: Preliminary Applicationmentioning

confidence: 72%

Occupancy of rooms in urban residential buildings by users in cold areas of China

Dong

Sun

2022

Build. Simul.

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Occupancy is used to represent the movements and locations of users among various zones of buildings, and it is the basis of all other daily energy consumption behaviors. This study investigated eight families in cold areas of China based on occupancy measurements obtained in four main rooms, i.e., living room, bedroom, kitchen, and bathroom. In particular, we analyzed the duration of user occupancy and hourly mean occupancy, and characterized their regular and random features. According to the results, we developed an event-based occupancy model using an inhomogeneous Markov chain, where the rooms were modeled and daily events were divided into three categories according to their randomness. We established a new method for conversion between event characteristic parameters and a transition probability matrix, as well as an overlap avoidance method for active events. The model was then validated using real data. The results showed that the model performed well in terms of two evaluation criteria. The model should improve the accuracy of simulations of occupancy.

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Section: Preliminary Applicationmentioning

confidence: 72%

Occupancy of rooms in urban residential buildings by users in cold areas of China

Dong

Sun

2022

Build. Simul.

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“…The study remarked that a bigger sample is necessary to extract safe conclusions. In [41] the authors aimed to assess the alteration between common types of occupancy models followed by the U.S. population via cluster analysis. For the purposes of the study, American Time Use Survey data was utilized that was collected between 2006-2017.…”

Section: Time Use Surveys and Questionnairesmentioning

confidence: 99%

A systematic review of time user surveys-questionnaires and monitoring variables used to track occupant behaviour in residential buildings

Mylonas

Tsangrassoulis

Pascual

2023

IOP Conf. Ser.: Earth Environ. Sci.

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In recent years, a growing body of literature has revealed that occupants and their interactions with the building systems and controls (window opening, thermostats, solar shading, etc.) have become a key factor influencing the building’s energy performance and their Indoor Environment Quality. Occupant behaviour can lead to major disparities between the predicted-simulated and the actual energy use in buildings, described by the term Energy Performance Gap, affecting possible strategies to reduce energy consumption. A potential solution to minimize the differences between the simulated and the real performance is by designing realistic models of occupants, which can be incorporated in a simulation code providing useful information for the design and/or assessment of new or existing buildings. Usually, occupant behaviour models are treated as static-deterministic schedules in building simulations, disregarding their stochastic and diverse nature. Accurate configuration of occupant models leads to fine-tuned simulations, enhancing the operation of building control systems. An occupant-centric building design results in building systems capable to meet occupants’ health and comfort requirements, minimizing at the same time the energy use. Although a number of techniques have been used to model occupancy behaviour, such as probabilistic and statistical models, in the present work, we systematically review the most recent techniques based on questionnaires and monitoring variables. These techniques are quite common and their application could help to better understand what drives occupants to interact with a building and its systems, making possible the design of more robust and realistic occupant models.

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“…Unlike the top-down method, the bottom-up method does not rely on historical energy consumption data; thus, the simulation results are more accurate in exceptional cases [51] , [52] . An increasing number of scholars have modelled the energy-consumption behaviour of residents, linking the energy-consumption behaviour of occupants to energy consumption, thereby calculating building energy consumption [53] , [54] , [55] , [56] .…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of the influence of the stay-at-home order on the electricity consumption in Chinese university dormitory buildings during the COVID-19 pandemic

Zhou

Luo

Liu

et al. 2022

Energy and Buildings

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Cluster analysis of occupancy schedules in residential buildings in the United States

Cited by 30 publications

References 37 publications

Occupancy of rooms in urban residential buildings by users in cold areas of China

Occupancy of rooms in urban residential buildings by users in cold areas of China

A systematic review of time user surveys-questionnaires and monitoring variables used to track occupant behaviour in residential buildings

Analysis of the influence of the stay-at-home order on the electricity consumption in Chinese university dormitory buildings during the COVID-19 pandemic

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