mlsanityData to 14 Oct 2026

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

arXiv 2608.20423 ↗First posted 19 Aug 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 39 references that are in the map. First posted on arXiv 19 Aug 2026.

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4 of 39 references.

References · 4

  1. 2016Benchmarking Deep Reinforcement Learning for Continuous Controlcited by 371
  2. 2020A Review of Deep Reinforcement Learning for Smart Building Energy Managementcited by 38
  3. 2019Deep Reinforcement Learning for Smart Home Energy Managementcited by 30
  4. 2021Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Veccited by 4

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  2. 2025Human-in-the-Loop AI for HVAC Management Enhancing Comfort and Energy Efficiency
  3. 2022Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models
  4. 2026PIRS: Physics-Informed Reward Shaping for SAC-Based Building Energy Management
  5. 2025TCEval: Using Thermal Comfort to Assess Cognitive and Perceptual Abilities of AI
  6. 2024Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature
  7. 2024Employing Federated Learning for Training Autonomous HVAC Systems
  8. 2025Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC