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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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reinforcement learningthermal comfortpersonalizationHVAC
2608.20423
Builder Relevance
2h ago80%
Abstract
This paper presents a two-stage personalised thermal comfort approach integrating multimodal physiological and environmental sensing with reinforcement learning-based decision-making.
Reality Card
Core Claim
The study successfully integrates reinforcement learning with physiological and environmental data to create a personalized thermal comfort system that adapts to individual needs.
Method / Result
The approach utilizes multimodal sensing to enhance thermal comfort prediction and intervention.
Limitations
The reliance on specific physiological data may limit generalizability across diverse populations.
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