Physical Sciences and Mathematics Commons^™

Predicting Energy Demand Peak Using M5 Model Trees, Sara S. Abdelkader, Katarina Grolinger, Miriam Am Capretz

Electrical and Computer Engineering Publications

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 …