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

TL;DR

This paper explores using deep LSTM neural networks and Wi-Fi data to accurately predict building occupancy levels at specific times and locations, enhancing smart building management without relying solely on traditional sensors.

Contribution

It introduces a novel application of LSTM models for occupancy prediction using Wi-Fi data, demonstrating significant improvements over ARIMA models in accuracy and computational efficiency.

Findings

LSTM combined model reduces computational resources by over 67%.

LSTM models significantly outperform ARIMA with up to 93.4% lower RMSE.

Wi-Fi data effectively supports occupancy forecasting in smart buildings.

Abstract

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.