A novel hybrid approach for wind speed prediction
This paper proposes a novel hybrid approach for wind speed prediction. SAM is applied to simulate and predict the seasonal component in wind speed series. ESM and RBFN mainly capture the linear and nonlinear patterns of the trend component in wind speed series. The wind speed datasets in the Hexi Corridor of China are used to demonstrate the proposed approach. It is concluded that the proposed approach can improve the prediction accuracy.
hybrid approach wind speed prediction seasonal adjustment exponential smoothing neural network