Title: Occupant counting modelling for intelligent buildings based on data from multiple WiFi sniffers

Authors: Ping Wang; Zhenya Zhang; Qiansheng Fang; Huaqian Cao; Si Chen

Addresses: Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, 856 Jinzhai South Road, Hefei, Anhui, China; School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui, China ' Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, 856 Jinzhai South Road, Hefei, Anhui, China; School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui, China ' Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, 856 Jinzhai South Road, Hefei, Anhui, China ' Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving, Anhui Jianzhu University, 856 Jinzhai South Road, Hefei, Anhui, China ' Anhui Institute of Building Research and Design, 7699 Fanhua Street, Hefei, Anhui, China

Abstract: Knowing the occupancy information in each room can create energy saving through intelligent control of HVAC systems. In this paper, a classification-based occupant counting method using multiple WiFi sniffers is proposed firstly to get a coarse estimation of occupancy. Then, to deal with the false negative problem, i.e., those occupants who do not carry a smartphone or if the WiFi module is not enabled and cannot be counted, a p-persistent frequent itemsets with 1-right-hand-side (RHS)-based occupant correction algorithm is further proposed to improve the occupant detection accuracy using association analysis. Finally, our methods are validated through real experiments. Results show that our classification-based occupant detection method using multiple WiFi sniffers outperforms the 1-WiFi-sniffer-based method, and the association analysis-based correction algorithm can improve the accuracy because it can see occupants in buildings that the naive WiFi-based occupant detection method cannot see, which makes it suffice to be a viable approach to occupant estimation for intelligent buildings.

Keywords: occupant counting; classification; WiFi sniffers; association analysis; frequent itemsets.

DOI: 10.1504/IJSPM.2020.110178

International Journal of Simulation and Process Modelling, 2020 Vol.15 No.4, pp.387 - 399

Received: 02 Apr 2019
Accepted: 06 Nov 2019
Published online: 08 Oct 2020 *

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