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arXiv · 2608.20423

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

Isibor Kennedy Ihianle·Emmanuel Manu·Ehsan Asnaashari·Mojgan Jadidi·Pedro Machado·Amrit Sagoo·Ahmad Lotfi
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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.

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Isibor Kennedy Ihianle, Emmanuel Manu, Ehsan Asnaashari, Mojgan Jadidi, Pedro Machado, Amrit Sagoo, Ahmad Lotfi. 2026-08-19. From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing. https://arxiv.org/abs/2608.20423

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