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《Journal of Shanghai Institute & Electric Power》 2005-04
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The Short-term Load Forcast Using C-means Clustering and BP Network

LI Qin~1,XU Qiang~2,WU Qiong~1 (1.School of Electric Power and Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China;2.Computer Science and Information Engineering College,Chongqing Technology and Business University,Chongqing 400067,China)  
This paper presents a short-term load forecasting method using fuzzy c-means clustering analysis and BP neural network.The historical load data are divided into several categories using fuzzy c-means clustering analysis,the corresponding BP neural network is built and then the Levenberg-Marquardt optimization to train the network is empleyed.The category coincident is found out with that of the daily load to be forecasted,and then the 96 points daily load is forecast with the corresponding BP network.The actual load forecasting results shows that the proposed method possesses faster training speed and greater forecasting accuracy.
【Fund】: 上海电力学院青年教师科研基金项目(F03010)
【CateGory Index】: TM715
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