An integrated fuzzy support vector regression and the particle swarm optimization algorithm to predict indoor thermal comfort

The thermal comfort indices are usually identified using empirical thermal models based on the human balanced equations and experimentations. In our paper, we propose a statistical regression method to predict these indices. To achieve this goal, first, the fuzzy support vector regression (FSVR) identification approach was integrated with the particle swarm optimization (PSO) algorithm. Then PSO was used as a global optimizer to optimize and select the hyper-parameters needed for the FSVR model. The radial basis function (RBF) kernel was used within the FSVR model. Afterward, these optimal hyper-parameters were used to forecast the thermal comfort indices: predicted mean vote (PMV), predicted percentage dissatisfied (PPD), new standard effective temperature (SET∗), thermal discomfort (DISC), thermal sensation (TSENS) and predicted percent dissatisfied due to draft (PD). The application of the proposed approach on different data sets gave successful prediction and promising results. Moreover, the comparisons between the traditional Fanger model and the new model further demonstrate that the proposed model achieves even better identification performance than the original FSVR technique.