A Review of Neural Networks for Buildings Occupancy Measurement

Abstract

Building occupancy measurements play a key role to minimize energy consumption and maintain occupants comfort. Accurate measurements support different applications related to the design and operating phases of smart buildings. A review of the usage of Neural Networks in building occupancy detection, counting, and prediction is proposed in this paper. This study discusses the background of the used algorithms and tries to analyze different approaches. The idea is to provide the reader with a deeper understanding of the usage of artificial neural network for building occupancy measurements along with analyzing the performance of each method.