Modeling and predicting occupancy profile in office space with a Wi-Fi probe-based Dynamic Markov Time-Window Inference approach

• Published in: Building and environment Vol. 124; pp. 130 - 142
• Main Authors: Wang, Wei, Chen, Jiayu, Song, Xinyi
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2017; Elsevier BV
• Subjects: Autoregressive moving-average models; Buildings; Control methods; Energy conservation; HVAC; HVAC equipment; Inference; Markov chains; Markov inference; Occupancy prediction; Overheating; Predictions; Regression analysis; Regression models; Studies; Support vector machines; Time series; Time window approach; Wi-Fi probe; Windows (intervals); Wireless access points; Wireless communications; Occupancy prediction; Markov inference; Time window approach; Wi-Fi probe
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2017.08.003

Demand-based HVAC control methods in buildings show great energy saving potential when accurate occupancy information is available. Appropriate service based on actual occupant demand could prevent unnecessary energy waste caused by system overcooling or overheating. Therefore, various occupancy detection approaches had attracted increasing attentions in recent years. Among them, Wi-Fi based detection approaches have been thoroughly discussed since Wi-Fi access points (APs) and wireless devices are ubiquitously used in modern buildings. Compared with traditional request and response based occupancy assessment, the newly developed Wi-Fi probe technology can actively scan Wi-Fi enabled devices even if they are not connected to the network. However, Wi-Fi probe detection still subjects to significant errors due to unstable signal and unpredictable occupant behavior. This study stresses the time-series and stochastic characteristics of detected signals and proposes a novel Dynamic Markov Time-Window Inference (DMTWI) model to predict reliable occupancy. The conventional Auto-Regressive Moving Average (ARMA) model and Support Vector Regression (SVR) model are also examined and compared with the proposed approach. Also, an on-site experiment was conducted to validate the proposed model, and the results reveal that the prediction accuracy is over 80% when x-accuracy tolerance is less than 4 for weekdays, 3 for holidays, and 2 for weekend days. •This paper proposed a novel occupancy detection method for HVAC systems.•The Wi-Fi probe is utilized to collect and predict occupancy information.•The proposed DMTWI method achieved high x-accuracy tolerance.•Three occupancy prediction models were compared.