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《Power System Technology》 2008-18
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Short-Term Load Forecasting Based on Least Squares Support Vector Machines

GENG Yan,HAN Xue-shan,HAN Li (School of Electrical Engineering,Shandong University,Jinan 250061,Shandong Province,China)  
A short-term load forecasting model and corresponding algorithm that is based on least squares support vector machines (LS-SVM) and integrates with rough sets (RS) theory and genetic algorithm (GA) is proposed. Because there are various factors impacting the accuracy of load forecasting, the historical data is pre-processed by RS theory and the reduction analysis is applied to condition attributes; the optimization for attribute reduction is implemented by GA to determine factors closely related to load which are taken as effective input variables of LS-SVM. Through adaptively optimizing the model parameters of LS-SVM by GA, the accuracy of load forecasting is improved; the dependence of LS-SVM on experience and the sightless selection of model parameters during the forecasting are avoided. Applying the proposed method to load forecasting of Shandong power grid, the results show that the proposed method is effective.
【Fund】: 国家自然科学基金资助项目(50377021 50677036)~~
【CateGory Index】: TM715
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