  Knowing how many people occupy a building, and where they are located, is a
key component of smart building services. Commercial, industrial and
residential buildings often incorporate systems used to determine occupancy.
However, relatively simple sensor technology and control algorithms limit the
effectiveness of smart building services. In this paper we propose to replace
sensor technology with time series models that can predict the number of
occupants at a given location and time. We use Wi-Fi data sets readily
available in abundance for smart building services and train Auto Regression
Integrating Moving Average (ARIMA) models and Long Short-Term Memory (LSTM)
time series models. As a use case scenario of smart building services, these
models allow forecasting of the number of people at a given time and location
in 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 for
every time scale, and a combined model for the three time scales. Our
experiments show that LSTM combined model reduced the computational resources
with 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) was
reduced by 88.2% - 93.4% for LSTM in comparison to ARIMA for the building
levels models and by 80.9% - 87% for the AP level models.
