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- W2377023348 abstract "Based on subsection forecasting and utilizing the advantages of back propagation(BP) neural network and radial basis function(RBF) neural network,such as short learning time of small-scale BP network and quick training of RBF network itself,a daily load forecasting method based on cascaded neural networks(CNN) is put forward.In this model non-load factors,i.e.,weather factors,day styles and so on which affect the changes of daily load,and historical load factors are added into BP and RBF neural networks and considered separately,thus the forecasting model is simplified further.The results of calculation example show that the proposed model converges quicker and is more efficient than common CNN,the forecasting accuracy is evidently improved." @default.
- W2377023348 created "2016-06-24" @default.
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- W2377023348 date "2009-01-01" @default.
- W2377023348 modified "2023-09-26" @default.
- W2377023348 title "A Daily Load Forecasting Method Based on Cascaded Back Propagation and Radial Basis Function Neural Networks" @default.
- W2377023348 hasPublicationYear "2009" @default.
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