Study of thermal sensation prediction model based on support vector classification (SVC) algorithm with data preprocessing

Abstract

In order to meet people's demand for comfort, indoor thermal environment often needs to be adjusted. Nevertheless, HVAC target parameters are often over-setted due to cognitive bias, resulting in an uncomfortable environment and waste of energy. Therefore, a thermal sensation prediction model is required to advise the setting of environmental and individual parameters. An accurate mode for predicting thermal sensation encountered severe challenges because of sensor errors, environmental noise, subjective thermal sensation differences, and sample data imbalance. The concrete aim of this paper is to verify the potential of using Support Vector Classification (SVC) algorithm to predict thermal sensation vote (TSV) based on Edited Nearest Neighbour (ENN) and Synthetic Minority Oversampling Technique (SMOTE), named combined ENN + SMOTE + SVC method. Firstly, for the problem of outliers in the dataset, the ENN method was adopted to clean the raw data. Secondly, SMOTE method is used to expand the training data which has the problem of sample imbalance. Finally, SVC algorithm is adopted to build a thermal sensation prediction model. The results show that the model built by the combined ENN + SMOTE + SVC method can achieve better performance than PMV index and other classic classification algorithms.