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《Journal of Hunan University(Natural Sciences)》 2008-06
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Power Demand Forecasting Based on BP Neural Network Optimized by Clonal Selection Particle Swarm

LI Xiang1,CUI Ji-feng1,2,XIONG Jun3,YANG Shu-xia1,YANG Shang-dong4(1.School of Business Administration,North China Electric University,Beijing 102206,China;2.State Grid Operation Company Limited,Beijing 100005,China;3.Chongqing Three Gorges Water Conservancy and Electric Power Co.Ltd,Chongqing 404000,China;4.State Power Economic Research Institute,Beijing 100761,China)  
Based on the ordinary BP algorithm,we first established a power demand forecasting model after the introduction of clonal selection particle swarm algorithm.Then,we identified the model's network structure by using the power demand's influential factors like the current GDP,the previous period GDP,population,the current changes of industrial structure,and the previous period changes of industrial structure as the input of the network.We used the power demand as the output of the network,and meanwhile chose the suitable number of hidden nodes.We repeated the optimization of the BP network's weight combination with the aid of a clonal selection particle swarm algorithm,and then adopted the weight optimized as the initial value of the BP neural network.We carried on the BP algorithm until the network met the training requirement.Finally,we used the recent years' annual data of relevant input and output variables to empirically forecast the power demand with the established model,and then compared the forecasting result with the ordinary BP neural networks.The comparison has shown that BP neural network based on clonal selection particle swarm has both fast training speed and small number of errors.The forecast precision has also been significantly improved,thus proving the validity of this model for forecasting power demand.
【Fund】: 国家自然科学基金资助项目(70501010)
【CateGory Index】: TP183;TM715
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