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Development of a Data-Driven Predictive Model of Clothing Thermal Insulation Estimation by Using Advanced Computational Approaches

Author

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  • Kyungsoo Lee

    (Building Energy Center, Energy Division, KCL (Korea Conformity Laboratories), Seoul 27872, Korea)

  • Haneul Choi

    (Department of Architecture & Architectural Engineering, Yonsei University, Seoul 03722, Korea)

  • Joon-Ho Choi

    (Department of Architecture & Architectural Engineering, Yonsei University, Seoul 03722, Korea
    School of Architecture, University of Southern California, Los Angeles, CA 90089, USA)

  • Taeyeon Kim

    (Department of Architecture & Architectural Engineering, Yonsei University, Seoul 03722, Korea)

Abstract

Clothing condition was selected as a key human-subject-relevant parameter which is dynamically changed depending on the user’s preferences and also on climate conditions. While the environmental components are relatively easier to measure using sensor devices, clothing value (clo) is almost impossible to visually estimate because it varies across building occupants even though they share constant thermal conditions in the same room. Therefore, in this study we developed a data-driven model to estimate the clothing insulation value as a function of skin and clothing surface temperatures. We adopted a series of environmental chamber tests with 20 participants. A portion of the collected data was used as a training dataset to establish a data-driven model based on the use of advanced computational algorithms. To consider a practical application, in this study we minimized the number of sensing points for data collection while adopting a wearable device for the user’s convenience. The study results revealed that the developed predictive model generated an accuracy of 88.04%, and the accuracy became higher in the prediction of a high clo value than in that of a low value. In addition, the accuracy was affected by the user’s body mass index. Therefore, this research confirms that it is possible to develop a data-driven predictive model of a user’s clo value based on the use of his/her physiological and ambient environmental information, and an additional study with a larger dataset via using chamber experiments with additional test participants is required for better performance in terms of prediction accuracy.

Suggested Citation

  • Kyungsoo Lee & Haneul Choi & Joon-Ho Choi & Taeyeon Kim, 2019. "Development of a Data-Driven Predictive Model of Clothing Thermal Insulation Estimation by Using Advanced Computational Approaches," Sustainability, MDPI, vol. 11(20), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5702-:d:276849
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    References listed on IDEAS

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    1. Park, June Young & Nagy, Zoltan, 2018. "Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2664-2679.
    2. Kwang Ho Lee & Stefano Schiavon, 2014. "Influence of Three Dynamic Predictive Clothing Insulation Models on Building Energy Use, HVAC Sizing and Thermal Comfort," Energies, MDPI, vol. 7(4), pages 1-18, March.
    3. Chen, Xiao & Wang, Qian & Srebric, Jelena, 2016. "Occupant feedback based model predictive control for thermal comfort and energy optimization: A chamber experimental evaluation," Applied Energy, Elsevier, vol. 164(C), pages 341-351.
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    1. Pisello, A.L. & Pigliautile, I. & Andargie, M. & Berger, C. & Bluyssen, P.M. & Carlucci, S. & Chinazzo, G. & Deme Belafi, Z. & Dong, B. & Favero, M. & Ghahramani, A. & Havenith, G. & Heydarian, A. & K, 2021. "Test rooms to study human comfort in buildings: A review of controlled experiments and facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).

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