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《Power System Technology》 2008-13
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A Hybrid Approach of Short-Term Load Forecasting Based on Generalized Learning Vector Quantity and Support Machine Vector

LUO Wei,YAN Zheng (School of Electronic Information and Electrical Engineering,Shanghai Jiaotong University, Minhang District,Shanghai 200240,China)  
A load characteristic clustering method based on generalized learning vector quantity (GLVQ) is proposed. This method not only considers the guidance of expert experiences but also possesses powerful nonlinear processing ability of artificial neural network (ANN). The induced load and comprehensive meteorological index can reflect the basic characteristics of daily load better; meanwhile, the dimensions of network input layer can be reduced. Practical short-term load forecasting results by the proposed method show that compared with pure support vector machine (SVM) algorithm and the hybrid algorithm of self-organized feature mapping (SOFM) and SVM, whether in clustering accuracy or in forecasting accuracy, the proposed hybrid short-term load forecasting method based on GLVQ and SVM possesses evident superiority.
【CateGory Index】: TM715
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