TY - JOUR
T1 - Building occupancy forecasting
T2 - A systematical and critical review
AU - Jin, Yuan
AU - Yan, Da
AU - Chong, Adrian
AU - Dong, Bing
AU - An, Jingjing
N1 - Funding Information:
Additionally, according to the literature review, building occupancy datasets are relatively sparse, making it difficult to validate research results. Therefore, a shared and authentic dataset is required. One project sponsored by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers collects valid global occupant behavior databases from researchers worldwide to help promote the standardization of occupancy forecast research.
Funding Information:
This research was supported by the National Key R&D (Research and Development) Program of China “Research and Integrated Demonstration on Suitable Technology of Net Zero Energy Building” (Grant No. 2019YFE0100300), the National Natural Science Foundation of China (Grant Number: 51778321 ), and the quantitative description and simulation methodology of occupant behavior in buildings. This research was also supported by the Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/11/15
Y1 - 2021/11/15
N2 - Indoor environment construction for occupants has high energy consumption; as such, occupancy plays a noteworthy role in the complete life cycle phase of buildings, including design, operation, and retrofitting. In the past few years, building occupancy, which is considered the basis of occupant behavior, has attracted increasing attention from researchers. There are increasing requirements for buildings to be both comfortable and energy efficient; with the development of detection methods and analyzing algorithms, occupancy prediction has become a topic of interest for building automation and energy conservation. Therefore, this article reviews the literature regarding future building occupancy predictions (forecasting). This review is distinguished from occupancy simulation and detection research and focuses on the research purpose, physical routine, and complete methodology of occupancy forecasting. First, the research purposes, including the application field and detailed requirements for occupancy forecasting, are summarized and analyzed. Next, an overall methodology of occupancy forecasting, including data acquisition, modeling techniques, and evaluation, is discussed in terms of issues affecting prediction performance. Finally, the current challenges and perspectives of occupancy forecasting are highlighted, considering the insights of natural characteristics, on-site implementation, valid dataset sharing, and research techniques. Overall, accurate and robust future occupancy predictions will help to improve building system operations and energy conservation.
AB - Indoor environment construction for occupants has high energy consumption; as such, occupancy plays a noteworthy role in the complete life cycle phase of buildings, including design, operation, and retrofitting. In the past few years, building occupancy, which is considered the basis of occupant behavior, has attracted increasing attention from researchers. There are increasing requirements for buildings to be both comfortable and energy efficient; with the development of detection methods and analyzing algorithms, occupancy prediction has become a topic of interest for building automation and energy conservation. Therefore, this article reviews the literature regarding future building occupancy predictions (forecasting). This review is distinguished from occupancy simulation and detection research and focuses on the research purpose, physical routine, and complete methodology of occupancy forecasting. First, the research purposes, including the application field and detailed requirements for occupancy forecasting, are summarized and analyzed. Next, an overall methodology of occupancy forecasting, including data acquisition, modeling techniques, and evaluation, is discussed in terms of issues affecting prediction performance. Finally, the current challenges and perspectives of occupancy forecasting are highlighted, considering the insights of natural characteristics, on-site implementation, valid dataset sharing, and research techniques. Overall, accurate and robust future occupancy predictions will help to improve building system operations and energy conservation.
KW - Building
KW - Energy conservation
KW - Forecast
KW - Occupancy prediction
KW - Occupant behavior
KW - Operation
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U2 - 10.1016/j.enbuild.2021.111345
DO - 10.1016/j.enbuild.2021.111345
M3 - Review article
AN - SCOPUS:85113329879
SN - 0378-7788
VL - 251
JO - Energy and Buildings
JF - Energy and Buildings
M1 - 111345
ER -