Abstract: To improve the accuracy of wind power forecasting, a combined ultra-short-term wind power prediction model integrating decomposition optimization and a multi-objective loss function is proposed. Firstly, the optimal number of modal components for variational modal decom position is dynamically searched based on an improved Gray Wolf Optimization Algorithm to achieve efficient decomposition of wind power series. The hyper-parameters of the prediction model are adaptively optimized to enhance the model’s generalization ability. Secondly, the improved gray wolf algorithm is introduced for adaptive hyper-parameter optimization, further improving generalization. A multi-objective loss function integrating prediction accuracy, stability, and grid-connection eligibility is designed. The prediction results of modal components are co-trained, and the final wind power prediction is reconstructed through weighted superposition of each component’s results. Validation experiments are conducted using actual wind power data from different seasons. The results show that the model’s optimal values of standardized mean absolute error, standardized root mean square error, and coefficient of determination reached 1.68%, 0.01%, and 99.45% respectively, significantly outperforming other models. Experimental results show that the proposed model has significant advantages in prediction accuracy and dynamic adaptability.

Key words: power prediction, variational mode decomposition, smart grid, gated cycle unit, multi-objective optimization