Smart System for Thermal Comfort Prediction on Residential Buildings Using Data-Driven Model with Random Forest Classifier

Abstract

Building area is a vital consumer of all globally produced energy. Structures of buildings absorb about 40 % of the total energy created which transcription about 30 % of the integral worldwide CO2 radiations. As such, reducing the measure of energy absorbed by the building area would incredibly help the much-crucial depletions in world energy utilization and the related ecological concerns. This paper presents a smart system for thermal comfort prediction on residential buildings using data driven model with Random Forest Classifier. The system starts by acquiring a global thermal comfort data, pre-processed the acquired data, by removing missing values and duplicated values, and also reduced the numbers of features in the dataset by selecting just twelve columns out of 70 columns in total. This process is called feature extraction. After the pre-processing and feature extraction, the dataset was split into a training and testing set. The training set was 70% while the testing set was 30% of the original dataset. The training data was used in training our thermal comfort model with Random Forest Classifier. After training, Random Forest Classifier had an accuracy of 99.99% which is about 100% approximately. We then save our model and deployed to web through python flask, so that users can use it in predicting real time thermal comfort in their various residential buildings.