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

• URL: http://arxiv.org/abs/2608.20423v1
• Date: Wed, 19 Aug 2026 09:39:15 GMT
• Title: From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing
• Abstract summary: This paper presents a two-stage personalised thermal comfort approach.<n>It integrates multimodal physiological and environmental sensing with reinforcement learning-based decision-making.
• Score: 2.335571888067613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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