发表机构
Nottingham Trent University; York University（诺丁汉特伦特大学; 约克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统HVAC系统无法捕捉个体生理变异性的问题，提出整合多模态感知与强化学习的两阶段个性化热舒适方法，助力开发更响应的建筑控制策略。
AI 中文摘要
个性化热舒适对居住者福祉及更具响应性的建筑控制策略开发至关重要，但传统暖通空调（HVAC）系统依赖静态设定点和人群级舒适模型，无法捕捉个体生理变异性。本文提出一种两阶段个性化热舒适方法，整合多模态生理与环境感知及基于强化学习的决策。
英文摘要
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.
