Short-term wind power prediction based on improved variational modal decomposition, least absolute shrinkage and selection operator, and BiGRU networks

Wind energy is a clean resource widely utilized as a renewable energy source. However, due to its inherent strong volatility and the multitude of influencing factors, it is challenging to accurately predict wind power. To address these issues, an IVMD-LASSO-BiGRU model, comprising Improved Variational Mode Decomposition (IVMD), Least Absolute Shrinkage and Selection Operator (LASSO), and Bidirectional Gated Recurrent Unit (BiGRU), is proposed for forecasting. Firstly, based on the sparse prior knowledge of each component constructed in the variational model by VMD, the optimal decomposition mode number K is determined at the inflexion point where the sparsity index shifts from rising to falling. The original wind power sequence is then decomposed into a series of Intrinsic Mode Functions (IMFs) using VMD with the optimal K value, thereby reducing the volatility of the original sequence. Secondly, LASSO is employed to select key features from meteorological data, historical wind power, and IMFs, thereby reducing the data dimension. Subsequently, BiGRU is utilized to fully extract the temporal features of the input data, establishing the mapping between input and output. Experimental results demonstrate that on three different datasets, the R2 values of the proposed forecasting method reach 0.9872, 0.9917, and 0.9941, respectively. Compared to the traditional BiGRU model, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) are reduced by an average of 57.56% and 58.88%, respectively. Thus, it is evident that the proposed method enhances the accuracy of short-term wind power forecasting, providing a basis for adjusting power generation plans.