基于时空图注意力的短期电力负荷预测方法  

A Short-term Power Load Forecasting Method Based on Spatiotemporal Graph Attention

作　　者：李文英 杨高才 文明 罗姝晨 于宗超 姜羽 王鼎湘 LI Wenying;YANG Gaocai;WEN Ming;LUO Shuchen;YU Zongchao;JIANG Yu;WANG Dingxiang(Economic and Technological Research Institute of Hunan Electric Power Company,Changsha 410000,China;College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)

机构地区：[1]国网湖南省电力有限公司经济技术研究院,湖南长沙410000 [2]湖南大学电气与信息工程学院,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2024年第2期57-67,共11页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金青年资助项目(62106072);能源互联网供需运营湖南省重点实验室(2019TP1053);国网湖南省电力有限公司科技项目(5216A221N008)。

摘　　要：准确的电力负荷预测对现代电力系统的安全经济运行至关重要.电力负荷预测可以表述为一个具有一定潜在空间依赖性的多变量时序预测问题.然而,大多数现有的电力负荷预测工作未能探索这种空间依赖关系.基于此,本文提出了一种基于时空图注意网络的短期电力负荷预测方法.提出一种基于时空图注意网络模块,该模块使用图注意层实现自适应的捕捉各用户间的潜在空间依赖性,同时使用门控卷积注意力层对各用户用电量在时间维度上进行自适应拟合,以提高网络的预测精度.实际数据实验表明,本文提出的模型整体预测精度提高明显,特别是在一定程度上缓解了长程预测精度恶化的问题,验证了所提方法的有效性与可行性.Accurate power load forecasting is crucial to the safe and economic operation of modern power systems.Power load forecasting can be expressed as a multivariable time series forecasting problem with certain potential spatial dependence.However,most existing power load forecasting work fails to explore this spatial dependency relationship.Based on this,this paper proposes a short-term power load forecasting method based on the spatiotemporal graph attention network.A spatiotemporal graph-based attention network module is proposed,which uses a graph attention layer to adaptively capture potential spatial dependencies between users.At the same time,a gated convolutional attention layer is used to adaptively fit the electricity consumption of each user in the time dimension to improve the prediction accuracy of the network.Actual data experiments show that the overall prediction accuracy of the model proposed is significantly improved,especially in alleviating the problem of deteriorating long-range prediction accuracy to a certain extent,verifying the effectiveness and feasibility of the proposed method.

关 键 词：电力负荷预测 小世界网络 时空图注意力 门控扩张因果卷积 

分 类 号：TM73[电气工程—电力系统及自动化]