期刊
ENERGY CONVERSION AND MANAGEMENT
卷 50, 期 1, 页码 90-96出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2008.08.033
关键词
Cooling load; Prediction; Support vector machine; Neural networks; Energy conservation
资金
- National Natural Science Foundation of China [50538040, 50720165805]
- China Scholarship Council [[2006]3037]
This study presents four modeling techniques for the prediction of hourly cooling load in the building. In addition to the traditional back propagation neural network (BPNN), the radial basis function neural network (RBFNN), general regression neural network (GRNN) and support vector machine (SVM) are considered. All the prediction models have been applied to an office building in Guangzhou, China. Evaluation of the prediction accuracy of the four models is based on the root mean square error (RMSE) and mean relative error (MIZE). The simulation results demonstrate that the four discussed models can be effective for building cooling load prediction. The SVM and GRNN methods can achieve better accuracy and generalization than the BPNN and RBFNN methods. (C) 2008 Elsevier Ltd. All rights reserved.
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