From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing

Abstract:Personalised thermal comfort is essential for occupant wellbeing and for the development of more responsive building-control strategies, yet conventional Heating, Ventilation, and Air Conditioning (HVAC) systems rely on static setpoints and population-level comfort models that fail to capture individual physiological variability. This paper presents a two-stage personalised thermal comfort approach integrating multimodal physiological and environmental sensing with reinforcement learning-based decision-making.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2608.20423 [cs.LG]

(or arXiv:2608.20423v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.20423 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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