From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing
From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing
从热偏好预测到自适应热干预：一种结合生理与环境感知的强化学习方法
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.
摘要： 个性化的热舒适度对于居住者的健康以及开发更具响应性的建筑控制策略至关重要。然而，传统的暖通空调（HVAC）系统依赖于静态设定点和群体层面的舒适度模型，无法捕捉个体生理的差异性。本文提出了一种两阶段的个性化热舒适度方法，将多模态生理与环境感知与基于强化学习的决策制定相结合。
Authors: Isibor Kennedy Ihianle, Emmanuel Manu, Ehsan Asnaashari, Mojgan Jadidi, Pedro Machado, Amrit Sagoo, Ahmad Lotfi
作者： Isibor Kennedy Ihianle, Emmanuel Manu, Ehsan Asnaashari, Mojgan Jadidi, Pedro Machado, Amrit Sagoo, Ahmad Lotfi
Submission Details: Computer Science > Machine Learning arXiv:2608.20423 (cs) [Submitted on 19 Aug 2026]
提交详情： 计算机科学 > 机器学习 arXiv:2608.20423 (cs) [2026年8月19日提交]