Extracting typical occupancy data of different buildings from mobile positioning data

Occupancy is one of the main factors affecting building energy consumption. The occupancy data, which refer to the occupancy number in this paper, has been widely used in the building simulation field. However, due to the stochastic nature of occupant behavior, it is hard to predict and measure how...

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Bibliographic Details
Published in:Energy and buildings 2018-12, Vol.180, p.135-145
Main Authors: Jiefan, Gu, Peng, Xu, Zhihong, Pang, Yongbao, Chen, Ying, Ji, Zhe, Chen
Format: Article
Language:English
Subjects:
Building energy simulation
Buildings
Case studies
Cluster analysis
Computer simulation
Data processing
Electronic devices
Energy consumption
Energy modeling
Human behavior
Location based services
Mobile device
Stochasticity
Typical occupancy data
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Summary:Occupancy is one of the main factors affecting building energy consumption. The occupancy data, which refer to the occupancy number in this paper, has been widely used in the building simulation field. However, due to the stochastic nature of occupant behavior, it is hard to predict and measure how many people stay in a given building. The rapid development of mobile Internet technology provides an efficient and convenient option for occupancy detection. This paper proposes a concept of typical occupancy data (TOD), which are extracted from real-time occupancy data collected by mobile devices. K-means algorithm is employed to generate the TOD data through cluster analysis. An energy performance model of an office building is used as a case study to demonstrate the effectiveness of the TOD data.
ISSN:0378-7788
1872-6178
DOI:10.1016/j.enbuild.2018.09.002