Proceedings of the IEEE International Conference on Machine Learning and Applications
Predicting energy demand peak is a key factor for reducing energy demand and electricity bills for commercial customers. Features influencing energy demand are many and complex, such as occupant behaviours and temperature. Feature selection can decrease prediction model complexity without sacriﬁcing performance. In this paper, features were selected based on their multiple linear regression correlation coefficients. This paper discusses the capabilities of M5 model trees in energy demand prediction for commercial buildings. M5 model trees are similar to regression trees; however they are more suitable for continuous prediction problems. The M5 model tree prediction was developed based on a selected feature set including sensor energy demand readings, day of the week, season, humidity, and weather conditions (sunny, rain, etc.). The performance of the M5 model tree was evaluated by comparing it to the support vector regression (SVR) and artificial neural networks (ANN) models. The M5 model tree outperformed the SVR and ANN models with a mean absolute error (MAE) of 8.94 compared to 10.02 and 12.04 for the SVR and ANN models respectively.