Thermal Comfort Index Estimation and Parameter Selection Using Fuzzy Convolutional Neural Network

In order to monitor the comfort level of the city, which depends on several thermal metrics, in many indoor and outdoor applications it is required to estimate the comfort level of the city in real-time. Out of the many thermal comfort indices proposed so far, predicted mean voter (PMV) is one of the widely used measures for both indoor and outdoor ambiances. Due to the complexity of calculating PMV in real-time, many techniques have been proposed to estimate it without using all the required parameters. So far fuzzy networks have shown the best results for PMV estimation because of its rule generation capability. Convolutional neural network (CNN) is an deep learning based technique to classify, or to estimate particular parameter by shrinking them to significant data-collections. In this work, we fuzzified the system before applying CNN for regression to estimate the PMV values. Simulation results show that the proposed model outperforms the existing ANFIS model for PMV estimation with a lower root mean square error value.