Artificial Intelligent Models for Improved Prediction of Residential Space Heating

Abstract

Using artificial intelligence (AI) models, cost effective electricity meter, and easily accessible weather data, this paper discusses a methodology for improved prediction of hourly residential space heating electricity use. Four AI models [back propagation neural network (BPNN), radial basis function neural network (RBFNN), general regression neural network (GRNN), and support vector regression (SVR)] were used for predicting hourly residential heating electricity use. For this study, a typical single-family house was used to obtain the data used for AI prediction models. Results showed SVR’s ability to predict hourly residential heating electricity use was better when compared with other AI models. Furthermore, through comparison of prediction performance in different time periods, additional investigation was conducted to evaluate the effect of dynamic human behaviors on the prediction accuracy of the AI models. Results revealed that dynamic human behaviors have a negative effect on the prediction performance.