Journal
BUILDING AND ENVIRONMENT
Volume 197, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2021.107810
Keywords
Occupancy prediction; Model predictive control (MPC); Machine learning; Rule-based control; Reactive control; Energy efficiency
Funding
- Concordia University -Canada
- Concordia Research Chair Energy Environment
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Intelligent buildings utilize data from occupancy detection and monitoring networks to predict occupancy profiles and adjust HVAC operations to minimize energy consumption while maintaining thermal comfort and indoor air quality. Various occupancy-based control strategies have been proposed in the literature, with a focus on classification and evaluation of these strategies.
Intelligent buildings have drawn considerable attention due to rapid progress in communication and information technologies. These buildings can utilize current and historical data, collected from occupancy detection and monitoring networks, to predict occupancy profiles and adjust heating, ventilating, and air conditioning (HVAC) operations accordingly. This adjustment aims to minimize the energy consumption of HVAC systems while maintaining an acceptable level of thermal comfort and indoor air quality. To provide a trade-off between these conflicting objectives, a variety of occupancy-based control strategies have been proposed in the literature. The present article aims to review the research works concerning occupancy-based control systems, classify them based on the integration of occupancy information with control systems and identify their strengths and limitations. Finally, research gaps in this field are discussed from different aspects, including performance evaluation metrics, control methods, occupancy models and buildings types. Future research directions are also proposed to fill the identified gaps.
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