Prediction of the thermal comfort indices using improved support vector machine classifiers and nonlinear kernel functions

In this study, we focus mainly on supervised learning machine where an instructor provides the output samples during the learning phase. Different sets of representative experimental factors, such as air temperature, mean radiant temperature, relative humidity, air velocity, metabolism and clothing value that affect a person’s thermal balance were used for training the SVM machine. The results show the best correlation between SVM predicted values with a polynomial kernel of the second order and those obtained from conventional thermal comfort, such as the Fanger model and the ‘2-Node’ model.

Source: Indoor and Built Environment - February 8, 2016 Category: Occupational Health Authors: Megri, A. C., El Naqa, I. Tags: Review Source Type: research