Occupancy Prediction in Buildings: State of the Art and Future Directions

In the contemporary era, a substantial portion of global energy consumption is allocated to residential and office buildings. Regrettably, a considerable amount of this energy is squandered due to inefficient utilization of electrical systems. One of the recognized approaches to curbing this wastage involves the detection, learning, and prediction of user presence within buildings, enabling proactive measures based on these forecasts. Forecasting the presence and occupancy of individuals within building environments can yield substantial energy savings and contribute to maintaining optimal comfort levels for the occupants. Additionally, such predictions hold significant potential for enhancing security and ensuring the safety of individuals within the buildings. Given the aforementioned aspects, this chapter aims to provide an overview of the prominent research conducted on occupancy forecasting in building environments. Initially, we will examine the key monitoring methods, based on Internet of Things technologies, employed for assessing presence in buildings. Subsequently, we will delve into some machine learning and deep learning algorithms utilized for predicting occupancy. Finally, we will explore potential future directions in this field.