Short-term wind speed forecasting by combination of masking signal-based empirical mode decomposition and extreme learning machine

摘要

According to the requirement of accurate prediction of the short-term wind speed series, this paper proposes a new short-term combination prediction model of the wind speed series by means of the masking signal-based empirical mode decomposition (MS-EMD) and the extreme learning machine (ELM). Firstly, because of the non-stationary characteristics of the wind speed series, the wind speed series is decomposed into several components with different frequency bands by the MS-EMD to reduce the non-stationary characteristics. Secondly, in order to avoid the randomness of input dimensionality selection of the ELM, the phase space of each component is reconstructed. Thirdly, the ELM model of each component is established to predict the wind speed series. Finally, the predicted results of each component are superimposed to get the final result. The simulation result verifies that the proposed combination forecasting model is able to excavate the wind speed series features effectively and has relatively high prediction accuracy.