广西师范大学学报（自然科学版）2026，Vol.44Issue(4)：56-70,15.DOI:10.16088/j.issn.1001-6600.2025091401
基于多目标优化的超短期风电功率预测模型
Ultra-short-term wind power prediction model based on multi-objective optimization
摘要
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.关键词
功率预测/变分模态分解/智慧电网/门控循环单元/多目标优化Key words
power prediction/variational mode decomposition/smart grid/gated cycle unit/multi-objective optimization分类
信息技术与安全科学引用本文复制引用
闫远洋,谢丽蓉,张龙军,任娟,黄晨晨,胡超..基于多目标优化的超短期风电功率预测模型[J].广西师范大学学报（自然科学版）,2026,44(4):56-70,15.基金项目
国家自然科学基金(62463030) （62463030）
新疆维吾尔自治区自然科学基金重点项目(2024D01D05) （2024D01D05）
天山英才-高层次领军人才项目(2022TSYCLJ0017) （2022TSYCLJ0017）
新疆维吾尔自治区重大科技专项项目(2022A01007-4) （2022A01007-4）
