Multi-Step Wind Speed Forecasting by Secondary Decomposition Algorithm and LSTM

Enhancing the reliability of wind speed forecasting is vital for efficient wind power generation. Given the wind's stochastic nature, preprocessing is crucial to obtain a clean wind speed series. This study introduces an innovative wind speed prediction model that integrates Variational Mode Decomposition (VMD), Symplectic Geometry Mode Decomposition (SGMD), and Long Short-Term Memory (LSTM). The model begins with VMD dividing the series into low- and high-frequency parts, then the SGMD further analyzes the high-frequency segment, and LSTM predicts results based on these components. Collaborative use of VMD and SGMD enables thorough decomposition of intricate wind speed data, while LSTM boosts the model's ability to capture patterns and dependencies. This hybrid model addresses the challenges posed by wind power uncertainty, aiming to efficiently integrate wind energy into power systems. The proposed hybrid model was compared to some benchmark models and outperformed them, reducing MAPE by 58% and RMSE by 31% for Dataset 1, and improving MAPE by 14% and RMSE by 36% for Dataset 2. The results confirm the competitive strength of the proposed strategy. Furthermore, the suggested two-stage decomposition technique demonstrates suitability for the examination of nonlinear characteristics in wind speed patterns.