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

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Building Energy Center, Energy Division, KCL (Korea Conformity Laboratories), Seoul 27872, Korea
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Department of Architecture & Architectural Engineering, Yonsei University, Seoul 03722, Korea
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School of Architecture, University of Southern California, Los Angeles, CA 90089, USA
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(20), 5702; https://doi.org/10.3390/su11205702
Received: 19 September 2019 / Revised: 13 October 2019 / Accepted: 14 October 2019 / Published: 15 October 2019
(This article belongs to the Section Energy Sustainability)
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. View Full-Text
Keywords: clothing thermal insulation; thermal comfort; human subject experiment; body mass index; human factors; user-centered approach; data mining clothing thermal insulation; thermal comfort; human subject experiment; body mass index; human factors; user-centered approach; data mining
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Lee, K.; Choi, H.; Choi, J.-H.; Kim, T. Development of a Data-Driven Predictive Model of Clothing Thermal Insulation Estimation by Using Advanced Computational Approaches. Sustainability 2019, 11, 5702.

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