PMV Dimension Reduction Utilizing Feature Selection Method: Comparison Study on Machine Learning Models

Kyung-Yong Park and Deok-Oh Woo ()
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Kyung-Yong Park: Center for Housing Environment Research and Innovation of Korea Land and Housing Research Institute, 66, Raon-ro, Sejong 30065, Republic of Korea
Deok-Oh Woo: College of Engineering, Lawrence Technological University, 21000 W 10 Mile Rd., Southfield, MI 48075, USA

Energies, 2023, vol. 16, issue 5, 1-14

Abstract: Since P.O. Fanger proposed PMV, it has been the most widely used index to estimate thermal comfort. However, in some cases, it is challenging to measure all six parameters within indoor spaces, which are essential for PMV estimation; a couple of parameters, such as Clo or Met, tend to show a large deviation in accuracy. For these reasons, several studies have suggested methods to estimate PMV but their accuracies were significantly compromised. In this vein, this study proposed a way to reduce the dimensions of parameters for PMV prediction utilizing the machine learning method, in order to provide fast PMV calculations without compromising its prediction accuracy. Throughout this study, the most influential features for PMV were pinpointed using PCA, Best Subset, and the Gini Importance, with each model compared to the others. The results showed that PCA and ANN achieved the highest accuracy of 89.70%, and the combination of Best Subset and Random Forest showed the fastest prediction performance among all.

Keywords: PMV; feature selection; machine learning; dimension reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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