Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM

Abstract

Accurate and robust wind speed forecasting is essential for the planning, scheduling and maintenance of wind power. In this study, a novel wind speed multistep prediction model is proposed by combing the VMD (Variational Mode Decomposition), SSA (Singular Spectrum Analysis), LSTM (Long Short Term Memory) network and ELM (Extreme Learning Machine), in which, the VMD is employed to decompose the original wind speed data into a series of sub-layers; the SSA is adopted to further extract the trend information of all the sub-layers; the LSTM network is used to complete the forecasting for the low-frequency sub-layers obtained by the VMD-SSA; and the ELM is used to complete the forecasting for the high-frequency sub-layers obtained by the VMD-SSA. To investigate the multistep prediction performance of the proposed models, eight models are included in the comparisons. The four experiments results show that: (a) among all the involved models, the proposed model has the best multistep prediction performance; (b) compared to the other involved models, the proposed model is more effective and robust in extracting the trend information.