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To settle the problem which the precision and generalization performance of forecast model is affected easily by input variable, the method which reconstructs the original input space of back-propagation neural network by principal component analysis that can eliminate the relevance of value is researched. The method can not only reduce duplicated information but also extract the leading factors....
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed...
Neural networks (NN) have been widely used for electricity forecasting, but some difficulties are still found. One of those difficulties is in choosing the optimal network parameter, which are strongly important to obtain accurate result. ldquoTrial and errorrdquo commonly used to set the parameter is ineffective in terms of processing time and the accuracy. In this paper, Taguchi method is employed...
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