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

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Computer Science > Machine Learning

arXiv:2608.20423 (cs)
[Submitted on 19 Aug 2026]

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

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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.
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.20423 [cs.LG]
  (or arXiv:2608.20423v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2608.20423
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Isibor Kennedy Ihianle [view email]
[v1] Wed, 19 Aug 2026 09:39:15 UTC (2,437 KB)
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