Affiliations: [a] Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, Clermont-Ferrand, France
| [b] LERMA-Lab, College of Engineering and Architecture, International University of Rabat, Sala El Jadida, Morocco
| [c]
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| [d] IA Laboratory, Faculty of science, Moulay Ismail University of Meknes, Zitoune, Meknès – Morocco | [e]
Univ. Bourgogne Franche-Comté, UTBM, FEMTO-ST UMR CNRS 6174, Belfort, France
Correspondence:
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Corresponding author: H. Elkhoukhi, Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal, F-63000 Clermont-Ferrand, France. E-mail: Hamza.elkhoukhi@gmail.com.
Abstract: Comprehensive occupancy information in smart buildings has become more imperative in order to develop new control strategies in energy management systems. Several techniques can be used to collect occupancy information considering accurate sensing techniques, such as passive infrared (PIR), carbon dioxide (CO2) and different types of cameras (i.e., thermal, or optical cameras). Recent studies show the usefulness of integrating occupancy information into energy management systems to reduce energy consumption while maintaining the occupants’ comfort. The purpose of this work is to elaborate a comprehensive review on occupancy detection systems in smart buildings. This study presents a set of comparison standards including methods, occupancy resolution, type of buildings and sensors. A classification of different approaches, which can be implemented and integrated into the building management system for detecting indoor occupancy, is introduced. Summary and discussions are given by highlighting the usefulness of machine learning for enabling predictive control of active systems in smart buildings.
Keywords: Occupancy detection, occupancy prediction, building energy management systems, data driven methods, machine learning
