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Knowing how many people occupy a building, and where they are located, is akey component of smart building services. Commercial, industrial andresidential buildings often incorporate systems used to determine occupancy.However, relatively simple sensor technology and control algorithms limit theeffectiveness of smart building services. In this paper we propose to replacesensor technology with time series models that can predict the number ofoccupants at a given location and time. We use Wi-Fi data sets readilyavailable in abundance for smart building services and train Auto RegressionIntegrating Moving Average (ARIMA) models and Long Short-Term Memory (LSTM)time series models. As a use case scenario of smart building services, thesemodels allow forecasting of the number of people at a given time and locationin 15, 30 and 60 minutes time intervals at building as well as Access Point(AP) level. For LSTM, we build our models in two ways: a separate model forevery time scale, and a combined model for the three time scales. Ourexperiments show that LSTM combined model reduced the computational resourceswith respect to the number of neurons by 74.48 % for the AP level, and by 67.13% for the building level. Further, the root mean square error (RMSE) wasreduced by 88.2% - 93.4% for LSTM in comparison to ARIMA for the buildinglevels models and by 80.9% - 87% for the AP level models.

Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings