Research on Short-Term Wind Power Forecasting Based on VMD-IDBO-SVM

To enhance the accuracy of wind power forecasting, this paper proposes a hybrid model that integrates Variational Mode Decomposition (VMD), Improved Dung Beetle Optimization (IDBO) and Support Vector Machine (SVM). First, to reduce the volatility and non-stationarity of wind power data, VMD is applied to decompose the original signal into several intrinsic mode functions (IMFs). Subsequently, the Dung Beetle Optimization (DBO) algorithm is improved using chaotic mapping, a Lévy flight search strategy and adaptive t-distribution. Finally, the penalty coefficient of the SVM is optimized using IDBO, and the VMD-IDBO-SVM model is constructed. This study proposes an improved IDBO algorithm and, for the first time, integrates VMD and IDBO-SVM within the context of wind power forecasting. Experimental results show that the proposed VMD-IDBO-SVM model achieves a MAE of 3.315, an RMSE of 4.130, and an R2 of 0.985 on test data from a wind farm, demonstrating a significant improvement compared with the traditional SVM model. It has demonstrated excellent stability and significance in both multi-time-slice validation and statistical testing.