Abstract:
Thermal Comfort indicates human interpretation of comfort level of an environment. Predicting
Thermal Comfort for a certain future date can have several applications. Predictive
Mean Vote (PMV) is one of the most used measure to express thermal comfort index, both
indoor and outdoor. Many of the parameters involved are needed to be synthesized which
adds to the complexity of it. Many techniques and algorithms to estimate it using only
some of the parameters involved have been proposed till date aiming to improve the accuracy.
Fuzzy Neural Network (FNN)s have been particularly successful in this scenario
generating suitable sets of rules. Improving the accuracy a step further while choosing
optimized number of parameters contribute to smoother and expanded applications. Convolutional
Neural Network (CNN) is an essential Deep Neural Network (DNN) technique.
It is primarily used shrink or convolve large data into smaller versions by keeping essential
details intact. These smaller versions are used to classify (or in some case estimate using
regression) using softmax layers or ReLU layers. In this work, focus was on combining
modified FNN with suitable layers of CNN and/or traditional neural network to estimate
PMV by regression with greater accuracy.
