A thermal response time ahead energy demand prediction strategy for building heating system using machine learning methods. (December 2017)

• Record Type: Journal Article
• Authors: Guo, Yabin; Li, Guannan; Chen, Huanxin; Wang, Jiangyu; Huang, Yao
• Is Part Of: Energy procedia. Volume 142(2017)
• Page Start: 1003
• Page End: 1008
• Publication Date: 2017-12
• Subjects: Building heating energy demand -- Prediction -- Machine learning -- Thermal response time -- Extreme learning machine; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.12.346
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: Energy demand prediction of building heating is conducive to optimal control, fault detection and diagnosis and building intelligent. In this paper, the prediction models are developed using machine learning methods including extreme learning machine (ELM), multiple linear regression, support vector regression and BP neural network. The feature variable sets are optimized through correlation analysis and supplementing indoor temperature. Besides, this paper proposed a strategy to determine the time ahead of prediction model. The thermal response time of building is used as the prediction time step of model. The prediction performances of ELM models with different hidden layer nodes are analyzed and contrasted. The actual data of the building heating using ground source heat pump system are collected and used to test the performances of the models. The results show that the thermal response time of the building is about 40 minutes. Four feature sets are obtained and performances of models with FS4 are better. For different machine learning methods, the performances of ELM models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the ELM model with FS4.