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《Proceedings of the CSEE》 2011-31
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Combined Model Based on EMD-SVM for Short-term Wind Power Prediction

YE Lin,LIU Peng(College of Information and Electrical Engineering,China Agricultural University,Haidian District,Beijing 100083,China)  
A wind power prediction method based on empirical mode decomposition(EMD) and support vector machine(SVM) is proposed to treat with the nonlinearity and nonstationarity of wind speed data.Firstly,the wind speed data is decomposed into a series of components with stationarity by using EMD to reduce the influence between different feature information.Then,different models were built and different kernel functions and parameters were chosen to deal with each group of data by using SVM in order to improve the forecasting accuracy.Finally,short term wind power forecasting was made based on wind speed data through a practical wind power curve.Case study was carried out to investigate the validity of the novel algorithm and the results illustrated that the forecasting error of EMD-SVM combined model decreased by 5%~10% compared to single statistics model.The proposed combined model can improve the short term forecasting accuracy of wind power effectively by tracking the change of wind power.
【Fund】: 国家自然科学基金项目(51077126 51174290);; 教育部科学技术重点研究项目(109017);; 教育部新世纪优秀人才支持计划(NCET-08-0543);; 北京市自然科学基金项目(3113029)~~
【CateGory Index】: TM715;TM614
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