用生理与环境数据，让空调自动调节温度，更懂每个人的体感舒适。
From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing
- 通过多模态传感+强化学习，动态预测个体热偏好。
- 相比传统恒温设定，热舒适度提升23%以上。
- 适合智能建筑、个性化温控系统研究者使用。
个性化热舒适对提升居住者健康与开发更智能的建筑控制策略至关重要，但传统暖通空调（HVAC）系统依赖静态设定值和群体级舒适模型，无法捕捉个体生理差异。本文提出一种两阶段个性化热舒适方法，结合多模态生理与环境传感数据，利用基于强化学习的决策机制实现自适应热干预。
原文摘要 · Abstract (English)
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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