SVMD-TF-QS: An efficient and novel hybrid methodology for the wind speed prediction

Abstract

Wind power is gaining significant attention as a renewable and environmentally friendly energy source. However, accurate forecasting of wind speed poses challenges due to its inherent variability and stochastic nature. To address this issue, a novel hybrid model (SVMD-TF-QS) for wind speed prediction (WSP) is proposed in this study. The model combines successive variational mode decomposition (SVMD) with a Transformer (TF) based model that incorporates a novel query selection (QS) mechanism. The SVMD component of the hybrid model offers several improvements, including enhanced mode extraction, adaptive mode determination, robustness against initial values of center frequencies, and improved computational efficiency. By decomposing the wind speed data using SVMD, the transformed data is then fed into the TF-QS model. The proposed approach effectively combines the benefits of the QS mechanism and the Transformer model to accurately predict wind speed while minimizing computational load. This is achieved by introducing a deterministic algorithm within the QS mechanism, which computes a sparse approximation of the attention matrix used in the Transformer model. This further enhances the predictive capabilities of the hybrid model. To evaluate its performance and generalization capability, extensive assessments are conducted using data from two wind farms located in Leicester and Portland. The assessments cover various time periods, including 5 min, 10 min, 15 min, 30 min, 1 h, and 2 h WSP intervals. The results of this study provide robust evidence supporting the effectiveness of the proposed hybrid model in WSP for the diverse wind farms and scenarios.