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《Power Demand Side Management》 2007-04
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Short-term load forecasting by using hybrid neural network and wavelet transform

KANG Li-feng1,YIN Cheng-qun2,BI Hong-bo2,QU Li2 (1. Hebei Institute of Architecture Civil Engineering,Zhangjiakou 075024,China; 2. North China Electric Power University,Baoding 071003,China)  
The accurate short-term load forecast is an accordance to make right marketing decision. The detailed process is as follows. Firstly, wavelet transform is employed to decompose the load sequence. To every decomposed sequence, according to each character the adequate effect factors are chosen. Secondly, information entropy and principal component analysis are combined for data reduction. By means of dynamic clustering each decomposed sequence is sample classified. Thirdly through gray relationship analysis it finds out some typical samples that are most close to forecasting-hour load model. Then we can train the neural network forecast model correspond to every decomposed sequence. At last through reconstructing sequence, the complete load forecast result is gotten. The actual load data testing shows that the forecast result through using this method is good.
【CateGory Index】: TM715
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