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Research on thermal comfort prediction method based on deep learning and thermal imaging technology
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Jinjun YAN1, Jiayun HU2, 3, Jiayun WANG2, 3, Kang LI2, 3
Fluid Machinery | 2026, 54(2) : 100 - 108
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Fluid Machinery | 2026, 54(2): 100-108
Research on thermal comfort prediction method based on deep learning and thermal imaging technology
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Jinjun YAN1, Jiayun HU2, 3, Jiayun WANG2, 3, Kang LI2, 3
Affiliations
  • 1China Three Gorges Renewables （Group） Co.，Ltd，Beijing 101100，China
  • 2Integrated New Energy Vehicle Thermal Management Evaluation，University of Shanghai for Science and Technology，Shanghai  200093，China
  • 3School of Energy and Power Engineering，University of Shanghai for Science and Technology，Shanghai  200093，China
Published: 2026-02-28 doi: 10.3969/j.issn.1005-0329.2026.02.011
Outline
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With the advancement of electrification and intelligent technologies，higher demands have been placed on human‑centric air conditioning systems.Based on image recognition technology，a deep learning model using ResNet50 as the backbone network and incorporating a spatial attention module （Attention-ResNet50） was constructed for human‑centric air conditioning systems.By collecting infrared image data under different thermal comfort conditions and classifying them into three categories，cold，comfort，and hot，for training and testing，this method significantly improved prediction accuracy.Experimental results showed that the introduction of the spatial attention mechanism increased the overall model accuracy by approximately 5%.The best model accuracy exceeded 94% for male subjects and surpassed 96% for female subjects.The study confirms the effectiveness of the attention mechanism in feature extraction，providing a reliable and universal technical pathway for personalized thermal comfort control.

deep learning  /  thermal imaging  /  attention mechanism  /  personalized thermal comfort model
严锦君, 胡家云, 王佳韵, 李康. 基于深度学习与热成像技术的热舒适度预测方法研究. 流体机械, 2026 , 54 (2) : 100 -108 . DOI: 10.3969/j.issn.1005-0329.2026.02.011
Jinjun YAN, Jiayun HU, Jiayun WANG, Kang LI. Research on thermal comfort prediction method based on deep learning and thermal imaging technology[J]. Fluid Machinery, 2026 , 54 (2) : 100 -108 . DOI: 10.3969/j.issn.1005-0329.2026.02.011
Appendix
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Year 2026 volume 54 Issue 2
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Article Info
doi: 10.3969/j.issn.1005-0329.2026.02.011
  • Receive Date：2025-08-17
  • Online Date：2026-10-22
  • Published：2026-02-28
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History
  • Received：2025-08-17
  • Revised：2026-01-19
Affiliations
    1China Three Gorges Renewables （Group） Co.，Ltd，Beijing 101100，China
    2Integrated New Energy Vehicle Thermal Management Evaluation，University of Shanghai for Science and Technology，Shanghai  200093，China
    3School of Energy and Power Engineering，University of Shanghai for Science and Technology，Shanghai  200093，China
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