Ultra-Short-Term Wind Power Point-Interval Prediction Based on Two-Layer Decomposition and MOISMA-SVM

The large-scale grid integration of new energy sources such as wind power is an important measure to accomplish the goal of the ″double carbon″. Reliable wind power prediction is important to ensure the safe operation of the power grid. Therefore, an ultra-short-term wind power combination prediction model is proposed. First, the original sequence of wind power is screened for outliers and corrected. The corrected data validates the objective law. Second, the two-layer decomposition algorithm is used to decompose the original sequence. The application of the modal decomposition algorithm can achieve sub-sequences with more predictable trends, which reduces the difficulty of wind power prediction. Subsequently, the Multi-Objective Improved Slime Mould Algorithm-Support Vector Machines (MOISMA-SVM) is constructed to accurately predict the subsequences and perform additive reconstruction. MOISMA optimizes multiple objective functions while optimizing SVM parameters to obtain wind power prediction results. Finally, the MOISMA-SVM model is applied to further correct the absolute error of these predictions, with the error correction results added to the initial wind power forecasts to produce the final point predictions. Experimental results demonstrate that the proposed model achieves the best error metric performance across both datasets, with Mean Absolute Errors (MAE) of 0.505 7 MW and 0.672 6 MW, representing improvements of 98.79% and 98.50% over the baseline SVM model, respectively. This highlights the high accuracy and robustness of the proposed approach. Based on the point prediction results, an improved kernel density estimation interval prediction model is also established, which generates prediction intervals with high reliability and narrow bandwidth. The Coverage Width-based Criterion (CWC) values for the two datasets are 0.002 4 and 0.002 8, respectively, enabling a more precise characterization of wind power fluctuations and enhancing the overall practicality of the model.