LS-SVM Short-Term Wind Speed Forecasting Based on Wavelet Decomposition Within the Bayesian Evidence Inference Framework
In view of the quasi-periodic, non-stationary and non-linear features of wind speed, the original wind speed sequence is decomposed into a series of sub-sequences based on the multiresolution analysis feature of wavelet. For each of these sub-sequences, a different tbrecasting model is established. The optimal parameters of every model can be found through the three-layer Bayesian evidence inference and they are used to establish the least squares support vector machine （LS-SVM） short-term wind speed forecasting model based on the wavelet decomposition and the Bayesian evidence inference framework, When the proposed method was applied in the one-hour-ahead wind speed prediction in a wind farm in the northeast region, the mean average percentage error of the predicted wind speed was only 7.63%, a large improvement of the prediction precision. The results verify the effectiveness of the proposed method.
NETL
NSTL
万方数据
维普
