Occupant Activity Detection in Smart Buildings: A Review

Abstract

Building management systems (BMS) in smart buildings are supposed to support the optimization of energy and resources consumption, while ensuring basic users’ comfort. A common and effective optimizing strategy is to detect, with high accuracy, room occupancies, events, and activities that occur within a building, to accordingly control the energy usage. Several approaches have been implemented to achieve this goal, combining many technologies (e.g., sensor networks, machine learning techniques) as well as new data sources (e.g., sensed data, social networks) allowing to better detect occupant activities. In this context, the purpose of this study is twofold:

(i) identify existing solutions related to capturing occupant activities and events to better manage energy usage and provide occupants’ comfort, and

(ii) pin down the lessons to learn from existing approaches and technologies in order to design better solutions in this regard.

We do not pretend to give an exhaustive revision, but throughout this review, we aim at showing that several data can significantly enrich the typology and content of information managed to detect occupant activities and highlight new possibilities in terms of activities diagnosis and analysis to generate more opportunities in optimizing the energy consumption and providing comfort in smart building.