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《Proceedings of the CSU-EPSA》 2015-01
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Research on Electrical Load Short-term Forecasting via Radial Basis Function Neural Network Designed by Affinity Propagation

HUI Lichuan;YU Miao;LIANG Zhirui;Faculty of Electrical and Control Engineering,Liaoning Technical University;Department of Electric Power Engineering,North China Electric Power University;  
In order to find the information of the load data more effectively and to improve the prediction accuracy of the radial basis function(RBF), a novel radial basis function neural network algorithm is proposed with the affinity propagation. The RBF algorithm uses the affinity propagation to cluster the sample data according to the internal similarity rule. Then the clustering centers of the sample data is obtained, which is the center vectors of the RBF neural network, and the width of the basis function can be set based on the distance of clustering centers. Finally the model training using the sample data and short-term forecasting are achieved. The loads of next day are forecasted by these methods to validate the method. It provides a new algorithm for the electrical load short-term forecasting.
【Fund】: 辽宁省大学生创新训练计划项目(201310147059);; 辽宁省自然科学基金项目(201102086);; 博士启动基金项目(2010413);; 校市场调研基金项目(SCDY2013038)
【CateGory Index】: TM715
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