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《Transactions of China Electrotechnical Society》 2015-09
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Short-Term Wind Speed Combined Prediction for Wind Farms Based on Wavelet Transform

Tian Zhongda;Li Shujiang;Wang Yanhong;Gao Xianwen;College of Information Science and Engineering Shenyang University of Technology;College of Information Science and Engineering Northeastern University;  
In order to improve short-term wind speed prediction accuracy of wind farms, a combined prediction method based on the wavelet transform is proposed. Firstly,the db3 wavelet is used for three-layer decomposition and reconstruction for short-term wind speed time series through Mallat algorithm. The approximation components and the detail components of the short-term wind speed are then obtained. Next,according to the different characteristics of these components,the least square support vector machine optimized by particle swarm algorithm and the autoregressive integrated moving average model are adopted as the predictive models for the approximate components and the detail components respectively. Then,the final predictive value of the short-term wind speed is obtained by the combination of the two components. The simulation results indicate that higher accuracy can be obtained in this prediction method.
【Fund】: 国家自然科学基金重点项目(61034005);; 辽宁省博士科研启动基金(20141070)资助
【CateGory Index】: TM614
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【Co-citations】
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1 CHEN Qinghong1,XING Linhua1,XIAO Jianhua2,MENG Anbo2,WANG Xinghua2,3(1.Jieyang Power Supply Bureau of Guangdong Power Grid Corporation,Jieyang,Guangdong 522000,China;2.Guangdong University of Technology,Guangzhou,Guangdong 510006,China;3.South China University of Technology,Guangzhou,Guangdong 510640,China);Study on Wind Power Short-time Power Prediction Based on Bayes Neural Network[J];Guangdong Electric Power;2013-01
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