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《Proceedings of the Chinese Society of Universities for Electric Power System and Automation》 2007-05
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Short-Term Load Forecasting Based on Improved Fuzzy Neural Network

GENG Wei-hua,SUN Qu,ZHANG Cui-xia,CHEN Xiao-yan (School of Electrical Engineering and Information,Sichuan University,Chengdu 610065,China)  
There are many factors influencing the accuracy of load forecasting,such as date type,holiday and weather.Besides,some other interferential factors,including power limitation and sustaining high temperature,also play an important role.According to the interferential factors above,a new short-term load forecasting approach based on improved fuzzy neural network is presented by introducing "intervening item".The basic principle,network model and forecasting process of the approach are explained in detail.Case studies show that the proposed approach has a higher forecasting precision compared with the other two methods.
【Fund】: 国家自然科学基金重大项目“电力系统广域安全防御体系基础理论及关键技术研究”子课题(50595412);; 国家重点基础研究发展规划项目(973)子课题
【CateGory Index】: TP183;TM715
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