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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...
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...
Short-term electricity load forecasting is important both from the technological and the economical point of view, but it is also a difficult work because the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. By studying the methods proposed by other scholars, a mew method, KPCA (kernel principal component analysis)...
This paper introduced the conjectured supply function equilibrium based model in order to construct the company's strategy model under the incomplete information. It can be used to apply in the market with inelastic electricity demand and it is more suitable to evaluate potential market power existed in real electricity market. On the basis of supply function equilibrium theory, it gives out the optimized...
In view of electricity customer credit evaluation lacking of precise index system and hardly quantifying subjective factors and experience factors, fuzzy expected value decision-making method modified by least squares support vector machine (LS-SVM) is presented. Firstly, electricity customer credit evaluation index system is constructed; the indices values and subjective experiences values are given...
This paper is to introduce a model. In the analysis of contract risk recognition, redundant variables in the samples spoil the performance of the SVM classifier and reduce the recognition accuracy. On the other hand, we usually canpsilat label one risk as absolutely good, or absolutely bad. In order to solve the problems mentioned above, this paper used rough sets (RS) as a preprocessor of SVM to...
The stable prices rose in the real estate market attracted a large amount of funds injected into it, to choose a good investment environment has been a keyto get profit from investment. In this paper, a Support Vector Machine (SVM) model is founded to do the evaluation. Based on the comprehensive evaluation index system of real estate investment environment, Rough set (RS) is introduced to reduce...
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