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

A new prediction method for thermal comfort indices is introduced. This method of prediction titled 'support vector machine (SVM)' uses learning as a process to emulate human intelligence. In this paper, more adequate nonlinear kernels have been used and the SVM has been improved to predict the thermal comfort indices accurately. 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.