Machine Learning for Activity Recognition in Smart Buildings: A Survey

Machine learning and data mining techniques have been widely used recently in several smart buildings applications. This is mainly due to the huge amount of data generated continuously by the smart sensors and meters deployed in new buildings. These data can be used to extract important knowledge about the building human part, for instance. Examples include the number of occupants and their activities which may provide crucial clues to deliver innovative end-user services to empower the building occupants by putting them in the loop of energy usage efficiency and supporting them to achieve their objectives by pointing out the impact of their actions. Estimating the number of occupants and recognizing their activities are also important inputs to develop advanced energy management systems (EMMS). Motivated by the various smart buildings and smart homes applications, the goal of this chapter is to overview several machine learning algorithms and provide a comprehensive review of such methods in activity recognition. Moreover, a case study which main goal is to estimate occupancy is detailed. Presenting this study is motivated by the fact that it could be extended and improved further for activity recognition.