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Study on Thermal Comfort Model Based on Genetic Algorithm with Backpropagation Neural Network

  • Yalong Yang
  • Dejian Hong
  • Rui ZhangEmail author
  • Qiansheng Fang
  • Xulai Zhu
  • Wenmiao Wu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)

Abstract

To reduce fossil fuel energy consumption and improve the using efficiency, it is of great significance to study the thermal comfort model based on multiple physiological parameters. Compared with the classical models, comfort can be reflected more accurate by using the thermal comfort model based on multiple physiological parameters. In this paper, the experiments were performed to verify the effectiveness of the thermal comfort model. In particular, to verify the practicability of the thermal comfort model based on multiple physiological parameters, the established thermal comfort model based on the genetic algorithm with a backpropagation neural network and the classical PMV were compared. The results indicate that the established thermal comfort model is reasonable, which provides a feasible option for achieving a comfortable indoor environment. Finally, it puts forward further study on the thermal comfort model based on more physiological parameters.

Keywords

Thermal comfort model Multiple physiological parameters Genetic algorithm 

Notes

Acknowledgements and Statements

This research was funded by the National Key Research and Development Program of China (2017YFC0704100), the National Key Technology Support Program of China (2015BAJ08B03), the Natural Science Foundation of Anhui Province, China (1508085QF131) and the Major Project on the Integration of Industry, Education and Research, Institute of plasma physics, Chinese Academy of Sciences, China (AJ-CXY-KF-17-36).

This study has been conducted with ethics approval obtained from the ethics committee: Prof. Yinfeng Zhu of the Department of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, China.

Participants are recruited from Anhui Jianzhu University, Hefei, China, and all participants have given their consent to use the dataset and disclose information relevant for research in this study.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yalong Yang
    • 1
    • 2
    • 3
  • Dejian Hong
    • 1
    • 2
  • Rui Zhang
    • 1
    • 2
    Email author
  • Qiansheng Fang
    • 1
    • 2
  • Xulai Zhu
    • 1
    • 2
  • Wenmiao Wu
    • 1
    • 2
  1. 1.Anhui Province Key Laboratory of Intelligent Building and Building Energy SavingAnhui Jianzhu UniversityHefeiChina
  2. 2.School of Electronic and Information EngineeringAnhui Jianzhu UniversityHefeiChina
  3. 3.Institute of Plasma Physics, Chinese Academy of SciencesHefeiChina

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