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

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.

Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning

“…Subsequently, the normalized data were downscaled using principal component analysis. The downscaled data simplified the computation and visualization to a certain extent, and the noise and redundant information in the original data could be removed [41,42]. A line graph of the cumulative variance and the number of principal components was plotted (Figure 5), and the number of principal components corresponding to the cumulative contribution rate of 0.95 was used as the optimal number of principal components.…”