摘要
It is difficult to predict wind speed series accurately due to the instability and randomness of the wind speed series.In order to predict wind speed,authors propose a hybrid model which combines the wavelet transform technique(WTT),the exponential smoothing(ES)method and the back propagation neural network(BPNN),and is termed as WTT-ES-BPNN.Firstly,WTT is applied to the raw wind speed series for removing the useless information.Secondly,the hybrid model integrating the ES method and the BPNN is used to forecast the de-noising data.Finally,the prediction of raw wind speed series is caught.Real data sets of daily mean wind speed in Hebei Province are used to evaluate the forecasting accuracy of the proposed model.Numerical results indicate that the WTT-ES-BPNN is an effective way to improve the accuracy of wind speed prediction.
