A novel energy demand prediction strategy for residential buildings based on ensemble learning. (February 2019)

• Record Type: Journal Article
• Authors: Huang, Yao; Yuan, Yue; Chen, Huanxin; Wang, Jiangyu; Guo, Yabin; Ahmad, Tanveer
• Is Part Of: Energy procedia. Volume 158(2019)
• Page Start: 3411
• Page End: 3416
• Publication Date: 2019-02
• Subjects: Building heating energy demand -- Prediction -- Machine learning -- Ensemble learning; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2019.01.935
• Languages: English
• ISSNs: 1876-6102

Abstract

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%.