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

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

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Abstract

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.

Authors:

^[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

Publication Date:: 2018-04-28

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

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)

OSTI Identifier:: 1506317

Alternate Identifier(s):: OSTI ID: 1548443

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: Building and Environment

Additional Journal Information:: Journal Volume: 138; Journal Issue: C; Journal ID: ISSN 0360-1323

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats


                    Wang, Wei, Chen, Jiayu, Hong, Tianzhen, and Zhu, Na. Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology.  United States: N. p., 2018. 
        Web.  doi:10.1016/j.buildenv.2018.04.034.

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                    Wang, Wei, Chen, Jiayu, Hong, Tianzhen, & Zhu, Na. Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology.  United States.  doi:10.1016/j.buildenv.2018.04.034.

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                    Wang, Wei, Chen, Jiayu, Hong, Tianzhen, and Zhu, Na. Sat .  
        "Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology".  United States.  doi:10.1016/j.buildenv.2018.04.034.  https://www.osti.gov/servlets/purl/1506317.

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@article{osti_1506317,

  title        = {Occupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology},

  author       = {Wang, Wei and Chen, Jiayu and Hong, Tianzhen and Zhu, Na},

  abstractNote = {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.},

  doi          = {10.1016/j.buildenv.2018.04.034},

  journal      = {Building and Environment},

  number       = C,

  volume       = 138,

  place        = {United States},

  year         = {2018},

  month        = {4}

}

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DOI: 10.1016/j.buildenv.2018.04.034

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