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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.
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