Modelling occupant behaviour in residential buildings: A systematic literature review

Abstract

This systematic literature review examines Occupant Behaviour (OB) modelling in residential buildings, focusing on its role in narrowing the energy performance gap and enhancing occupant comfort. It provides an overview of methodologies from simple statistical models to advanced machine learning algorithms. We conducted a chronological literature review on OB modelling in residential buildings, identifying the algorithms and the main remarks and limitations of each study. We also present sample populations for each study, offering a comprehensive view of OB modelling's evolution. Our study shows a trend towards incorporating intelligent technologies like IoT and Artificial Intelligence, highlighting an evolution towards more advanced and precise techniques. The review evaluates studies utilizing monitoring, diary, or survey data to improve behavioural model accuracy, covering aspects such as occupant presence, window manipulation, lighting and shading regulation, thermostat adjustment, and usage patterns of domestic hot water and appliances. The review underscores the importance of standardized data collection and integrating OB into building standards. It advocates for adaptive OB models leveraging IoT data with privacy considerations and calls for comparative analysis of modelling techniques. Future research should develop adaptable occupancy profile repositories and integrated modelling frameworks to address OB complexities.