A novel energy demand prediction strategy for residential buildings based on ensemble learning

Abstract

Energy demand prediction is able to improve the energy efficiency and energy savings of the residential buildings. In this article, the prediction models are developed based on ensemble learning methods which combined extreme gradient boosting, extreme learning machine, multiple linear regression with support vector regression. According to the importance analysis of the random forest method, the optimal set of feature variables is selected. Besides, a historical energy comprehensive variable named EWMA was added in the prediction models to improve the prediction accuracy. A ground source heat pump for residential buildings located in Henan, China, is selected as a model for examining 2-hour ahead heating load forecasting. Results showed that the proposed prediction model based on ensemble learning could reduce the MAE of the testing set prediction result, which ranged from 29.1% to 70%.