计算机工程2026，Vol.52Issue(9)：436-448,13.DOI:10.19678/j.issn.1000-3428.0070417
领域先验知识融合视角下的短期电力负荷预测研究
Research on Short-term Electric Power Load Forecasting from the Perspective of Integrating Domain Prior Knowledge
摘要
Abstract
Electric power load forecasting is a crucial step in optimal dispatching of power grids.However,when confronted with complex dynamic environments and incompletely stationary load characteristics,existing purely data-driven methods and strategies incorporating domain knowledge insufficiently capture long-term trends and periodic patterns,which affects forecasting accuracy and robustness.To address this issue,an electric power load forecasting model based on the integration of domain prior knowledge,named DPK-ELF,is proposed.This model utilizes a prior knowledge extraction module to thoroughly analyze the dynamic behavioral characteristics of time-series data,constructs domain prior knowledge specific to the given data,and employs a dynamic piecewise stacking average smoothing method to extract prior trends in electric power loads.The prior-trend decomposition module decomposes the electric power load series into prior smoothed trends and residual local random fluctuations,which are then combined with the PatchTST data-driven model for forecasting.Soft constraint optimization techniques are adopted during the model training phase using domain prior knowledge as boundary constraints in the loss function to enhance the robustness of the model.Experiments on four publicly available electric power load datasets reveal that DPK-ELF outperforms comparison models such as PatchTST,DLinear,Autoformer,and Informer across three key performance metrics:Mean Squared Error(MSE),Mean Absolute Error(MAE),and Relative Squared Error(RSE).In the Australian electricity price and power load dataset,compared with the PatchTST model,DPK-ELF achieves improvements of 28.31％,19.57％,and14.94％in MSE,MAE,and RSE,respectively.In the PDB power demand dataset,compared with the PatchTST model,DPK-ELF achieves improvements of 12.25％,7.77％,and 6.29％in MSE,MAE,and RSE,respectively.These results demonstrate the significant advantages of the DPK-ELF model in improving forecasting accuracy.关键词
电力负荷预测/领域先验知识/趋势分解/PatchTST模型/软约束优化Key words
electric power load forecasting/domain prior knowledge/trend decomposition/PatchTST model/soft constraint optimization分类
信息技术与安全科学引用本文复制引用
肖斌,谢珊,汪敏,刘德琦,姚瑞滢,李雨茹..领域先验知识融合视角下的短期电力负荷预测研究[J].计算机工程,2026,52(9):436-448,13.基金项目
国家自然科学基金(62006200) （62006200）
四川省科技计划项目(2024NSFSC2050). （2024NSFSC2050）
