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Research on Human Thermal Comfort Model Based on Multiple Physiological Parameters

  • Yalong Yang
  • Wenmiao Wu
  • Qiansheng FangEmail author
  • Xulai Zhu
  • Rui Zhang
  • Mingyue Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 890)

Abstract

Currently, the demand on thermal comfort of architectural environment is becoming higher and higher, the establishment of thermal comfort model based on the physiological parameters plays an important role in improving the indoor thermal comfort and building energy efficiency. The mean skin temperature, skin conductance, and heart rate are three main physiological parameters used to characterize the thermal comfort state of the human body, which will be of great significance to establish the thermal comfort model. In this paper, the physiological experimental program was designed in detail and the subjective questionnaires ruler was classified involving thermal sensation, thermal comfort, and sweat rate. Then, the human subjective thermal response distribution and the regular pattern were analyzed according to the vote on the state of thermal comfort and physiological parameters of subjects under five experimental conditions. As a result, the multiple physiological modeling of indoor human thermal comfort was performed by using partial least squares (PLS) method based on mean skin temperature, skin conductance, and heart rate. Furthermore, the experiments aimed at evaluating the accuracy of the established indoor human thermal comfort model were performed, the results indicate the accuracy of the established model is satisfactory.

Keywords

Thermal comfort Partial least squares method Physiological parameter Predicted mean vote 

Notes

Acknowledgements and Statements

This research was funded by the National Key Research and Development Program of China (2017YFC0704100) and 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.

References

  1. 1.
    (Jerry) Yu, Z., Yang, B., Zhu, N., Olofsson, T., Zhang, G.: Utility of cooling overshoot for energy efficient thermal comfort in temporarily occupied space. Build. Environ. 109(15), 199–207 (2016)Google Scholar
  2. 2.
    Lawrence, R., Keime, C.: Bridging the gap between energy and comfort: post-occupancy evaluation of two higher-education buildings in Sheffield. Energy Build. 130(15), 651–666 (2016)Google Scholar
  3. 3.
    Embaye, M., AL-Dadah, R.K., Mahmoud, S.: Numerical evaluation of indoor thermal comfort and energy saving by operating the heating panel radiator at different flow strategies. Energy Build. 121, 298–308 (2016)Google Scholar
  4. 4.
    Huang, Y., Niu, J.: A review of the advance of HVAC technologies as witnessed in ENB publications in the period from 1987 to 2014. Energy Build. 130(15), 33–45 (2016)Google Scholar
  5. 5.
    He, Y., Li, N., Peng, J., Zhang, W., Li, Y.: Field study on adaptive comfort in air conditioned dormitories of university with hot-humid climate in summer. Energy Build. 119, 1–12 (2016)Google Scholar
  6. 6.
    Chuen, L., Sears, D., McAdams, S.: Psycho-physiological responses to auditory change. Psycho-physiology 53(6), 891–904 (2016)Google Scholar
  7. 7.
    Alfano, F.R.D., Palella, B.I., Riccio, G.: Thermal environment assessment reliability using temperature humidity indices. Ind. Health 49(1), 95–106 (2011)CrossRefGoogle Scholar
  8. 8.
    de Paulo, J.M., Barros, J.E.M., Barbeira, P.J.S.: A PLS regression model using flame spectroscopy emission for determination of octane numbers in gasoline. Fuel 176, 216–221 (2016)Google Scholar
  9. 9.
    Hussin, L.X.A., Salleh, E., HY Chan, S. Mat.: The reliability of predictive mean vote model predictions in an air-conditioned mosque during daily prayer times in Malaysia. Archit. Sci. Rev. 58(1), 67–76 (2015)Google Scholar
  10. 10.
    Schweiker, M., Shukuya, M.: Adaptive comfort from the viewpoint of human body energy consumption. Build. Environ. 51, 351–360 (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yalong Yang
    • 1
    • 2
    • 3
  • Wenmiao Wu
    • 1
    • 2
  • Qiansheng Fang
    • 1
    • 2
    Email author
  • Xulai Zhu
    • 1
    • 2
  • Rui Zhang
    • 1
    • 2
  • Mingyue Wang
    • 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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