Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology

Wang, Wei; Chen, Jiayu; Hong, Tianzhen; Zhu, Na

doi:10.1016/j.buildenv.2018.04.034

Title: Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology

Journal Article · Sat Apr 28 00:00:00 EDT 2018 · Building and Environment

DOI:https://doi.org/10.1016/j.buildenv.2018.04.034· OSTI ID:1506317

^[1];

^[2];

^[3]; Zhu, Na ^[4]

City Univ. of Hong Kong, Kowloon (Hong Kong). Dept. of Architecture and Civil Engineering; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Division
City Univ. of Hong Kong, Kowloon (Hong Kong). Dept. of Architecture and Civil Engineering
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Division
Huazhong Univ. of Science and Technology, Wuhan, Hubei (China). Dept. of Building Environment and Energy Engineering, School of Environment of Science and Engineering

Accurate occupancy prediction can improve facility control and energy efficiency of buildings. In recent years, buildings’ exiting WiFi infrastructures have been widely studied in the research of occupancy and energy conservation. However, using WiFi to assess occupancy is challenging due to that occupancy information is often characterized stochastically and varies with time and easily disturbed by building components. To overcome such limitations, this study utilizes WiFi probe technology to actively scan WiFi connection requests and responses between access points and network devices of building occupants. With captured signals, this study proposed a Markov based feedback recurrent neural network (M-FRNN) algorithm to model and predict the occupancy profiles. One on-site experiment was conducted to collect ground truth data using camera-based video analysis and the results were used to validate the M-FRNN occupancy prediction model over a 9-day measurement period. From the results, the M-FRNN based occupancy model using WiFi probes shows best accuracies can reach 80.9%, 89.6%, and 93.9% with a tolerance of 2, 3, and 4 occupants respectively. In conclusion, this study demonstrated that WiFi data coupled with stochastic machine learning system can provide a viable alternative to determine a building's occupancy profile.

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Research Organization:: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1506317

Alternate ID(s):: OSTI ID: 1548443

Journal Information:: Building and Environment, Vol. 138, Issue C; ISSN 0360-1323

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 70 works

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