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| Short-term Wind Speed Forecasting Based on EMD-RBFNN |
| Yin Dongyang, Sheng Yifa, Li Yongsheng, Xie Qutian |
| College of Electrical Engineering, University of South China, Hengyang, Hu’nan 421001 |
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Abstract Aiming at the nonlinear and nonstationary of wind speed sequences, a novel method based on empirical mode decomposition (EMD) and radial basis function neural network (RBFNN) is proposed to improve the precision of short-term wind speed forecasting. The wind speed data is decomposed into a series of intrinsic mode function (IMF) components with similar time-frequency characteristics and stationary by using EMD to achieve the stationary of the wind speed data. The IMF components are predicted by RBFNN based on the time-frequency characteristics of different IMF components. The orthogonal least squares (OLS) is adopted to minimize the error rate. Finally, the each prediction results of IMF-RBFNN are restructured to obtain the last prediction result. The short-term wind speed forecasting system based on interactive GUI is designed and implemented. Forecasting results show that the EMD-RBFNN combined model can improve the forecasting accuracy of short term wind speed and is of a certain practical value.
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Published: 11 June 2014
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2014, Vol. 15 
