全球能源互联网2026，Vol.9Issue(3)：345-360,16.DOI:10.19705/j.cnki.issn2096-5125.20250457
温差驱动下基于多任务学习及可解释机器学习的综合能源系统多元负荷预测
Multi-variable Load Forecasting of Integrated Energy System Based on Multi-task Learning and Interpretable Machine Learning Driven by Temperature Difference
摘要
Abstract
With the advancement of the"dual carbon"goals and the rapid development of integrated energy systems,achieving the safe and economical operation of integrated energy systems has placed higher demands on the accuracy of multiple load forecasting.To address the shortcomings of existing research in handling load volatility and coupling,this paper proposes a short-term multiple load forecasting model for integrated energy systems(IES)based on variational mode decomposition(VMD),long short-term memory(LSTM),support vector machine(SVM),and Transformer.First,the influencing factors of multiple loads are analyzed,and the"12-hour temperature difference"is innovatively introduced as a key meteorological feature to capture abnormal load fluctuations caused by drastic temperature changes.Calendar information is quantified using one-hot encoding.Next,the original load series are decomposed using VMD.Then,the sparrow search algorithm(SSA)is employed to optimize SVM parameters and predict each IMF component,which serves as high-level feature inputs.Finally,a multi-task learning(MTL)framework is constructed,where the shared layer uses LSTM to explore the coupling relationships among multiple loads,and the task-specific layers employ Transformer encoders to capture the temporal dependencies of each load for predictions.Additionally,SHAP analysis is used to evaluate the impact of feature variables on the forecasting results.Using actual data from an industrial park in Lanzhou,China,as a case study,the experimental results demonstrate that the proposed model effectively reduces the mean absolute percentage error of load forecasting and achieves satisfactory prediction accuracy.关键词
综合能源系统/Transformer网络/长短期记忆网络/多元负荷预测/SHAP分析Key words
integrated energy system/transformer network/long short-term memory network/multi-load forecasting/SHAP analysis分类
能源科技引用本文复制引用
牛东晓,周泽颖,于霄宇,许晓敏..温差驱动下基于多任务学习及可解释机器学习的综合能源系统多元负荷预测[J].全球能源互联网,2026,9(3):345-360,16.基金项目
国家自然科学基金项目(72472050) （72472050）
国家电网有限公司总部科技项目(5400-202455364A-3-1-DG). National Natural Science Foundation of China(72472050) （5400-202455364A-3-1-DG）
Science and Technology Project of SGCC Headquarters(5400-202455364A-3-1-DG). （5400-202455364A-3-1-DG）
