Occupants’ behaviour is a critical factor influencing building energy consumption, characterized by its complexity, variability and stochastic nature. Residential buildings pose unique challenges due to more diverse and privacy-sensitive patterns of occupants’ behaviours compared to offices. Traditional building performance simulations often rely on fixed schedules or simplified assumptions, leading to significant deviations from actual energy usage. Notably, behavioural differences can cause energy consumption in households to vary by as much as 50–80%. The use of calibrated occupants’ behaviours models has been shown to reduce simulation errors by 10–20%. This study presents a systematic review from three key perspectives: data collection methods, occupants’ behaviours modelling approaches and energy simulation tools. Data acquisition techniques are evaluated in terms of their advantages, limitations and application scenarios. Behaviour models are classified into fixed schedule, data-driven and stochastic process types, addressing both occupants’ movement and energy-use behaviours. Additionally, a comparative analysis of mainstream energy simulation software has been conducted, focusing on their suitability for occupant-integrated modelling. This review aims to provide a theoretical foundation and practical reference for improving the accuracy of occupants’ behaviour representation in building energy simulations.
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