期刊
APPLIED ENERGY
卷 86, 期 10, 页码 2249-2256出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2008.11.035
关键词
Support vector machine; Building; Cooling load; Prediction; Artificial neural network
资金
- National Natural Science Foundation of China [50538040, 50720165805]
- China Scholarship Council [[2006]3037]
In this paper, support vector machine (SVM) is used to predict hourly building cooling load. The hourly building cooling load prediction model based on SVM has been established, and applied to an office building in Guangzhou, China. The simulation results demonstrate that the SVM method can achieve better accuracy and generalization than the traditional back-propagation (BP) neural network model, and it is effective for building cooling load prediction. (C) 2008 Elsevier Ltd. All rights reserved.
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