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Buildings consume about 40 % of globally-produced energy. A notable amount of this energy is used to provide sufficient comfort levels to the building occupants. Moreover, given recent increases in global temperatures as a result of climate change and the associated decrease in comfort levels, providing adequate comfort levels in indoor spaces has become increasingly important. However, striking a balance between reducing building energy use and providing adequate comfort levels is a significant challenge. Conventional control methods for indoor spaces, such as on/off, proportional-integral (PI), and proportional-integral-derivative (PID) controllers, display significant instabilities and frequently overshoot thermostats, resulting in unnecessary energy use. Additionally, conventional building control methods rarely include comfort regulatory schemes. Consequently, recent research efforts have focused on the use of advanced artificial intelligence (AI) methods to optimize building energy usage while maintaining occupant thermal comfort. We present a review of the current AI-based methodologies being used to enhance thermal comfort in indoor spaces. we focus on thermal comfort predictive models using diverse machine learning (ML) algorithms and their deployment in building control systems for energy saving purposes. We then discuss gaps in the existing literature and highlight potential future research directions.

openalex: W3001840828