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《Journal of North China Electric Power University(Natural Science Edition)》 2019-01
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Ultra-short-term Wind Speed Prediction Based on VMD and Improved ARIMA Model

ZHAO Zheng;WANG Xiangshuo;QIAO Jintao;School of Control and Computer Engineering,North China Electric Power University;  
To solve the problem of nonlinearity and volatility of wind speed series,this paper proposed a wind speed prediction model on the basis of variational mode decomposition and the improved differential auto regressive moving average model. Firstly,the signal was decomposed from low frequency to high frequency by variational mode decomposition( VMD). Each component had a limited bandwidth with different center frequencies. Then ARIMA models of each component were established. Because the residual sequence of each component may have heteroscedasticity,the GARCH model was then introduced to eliminate the heteroscedasticity. And the ARIMA-GARCH model was established. Finally,the prediction results of each component were superimposed to obtain the final prediction value. The results show that the proposed model has high accuracy in the ultra-short-term wind speed prediction.
【Fund】: 中央高校基本科研业务费专项资金资助项目(2017MS133);; 河北省自然科学基金资助项目(G2016502009)
【CateGory Index】: TM614
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