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Short-term load forecast based on combination of wavelet transform and hybrid neural network

YIN Cheng-qun,KANG Li-feng,LI Li,WANG Hong-yun(Department of Electronic and Communication Engineering,North China Electric Power University,Baoding 071003,China)  
A hybrid load forecast method is put forward.The character analysis is carried out with wavelet decomposition for each load subsequence and influencing factors are thus determined,from which main factors are selected using the information entropy method and their relativity is eliminated using the improved principal component analysis method.The dynamic clustering analysis is used to divide the historical load data into several categories and the grey relative analysis to pick out one as the typical sample set,which is most similar to the forecasting load mode.The ant colony optimization algorithm is then used to train the corresponding neural network model of each decomposed subsequence and the wavelet reconstruction is used to achieve final forecasts.Actual loads of a district in 1999 are taken for verification,which shows the proposed method is rational and effective.
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