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Personalized or pragmatic? A critical review of machine learning and metric-based models for indoor thermal comfort estimation

delete2026-08-04
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PRE
AI
R
Rui Li
H
Haibo Feng *
DOI：10.1016/j.buildenv.2026.115080delete
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Abstract

Abstract

En 中文
• Review recent advances in thermal comfort modeling from two major paradigms. • Analyze post-2021 progress in machine learning-based personalized comfort models. • Revisit PMV-based models with a focus on sensor integration and model refinements. • Identify trade-offs between personalization, explainability, and scalability. • Outline future directions for human-centered and deployment-ready comfort modeling.

Journal

Building and Environment cover
Building and Environment
IF:
7.6
Papers:
1.3W
Citations:
6.6W

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