[Submitted on 19 Aug 2026]
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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.
Submission history #
From: Isibor Kennedy Ihianle [[view email](/show-email/8e7b6e66/2608.20423)]
**[v1]** Wed, 19 Aug 2026 09:39:15 UTC (2,437 KB)
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