湖南大学学报（自然科学版）2024，Vol.51Issue(2)：57-67,11.DOI:10.16339/j.cnki.hdxbzkb.2024226
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
摘要
Abstract
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Key words
electric load forecasting/small-world network/spatiotemporal graph attention/gated convolutional attention分类
动力与电气工程引用本文复制引用
李文英,杨高才,文明,罗姝晨,于宗超,姜羽,王鼎湘..基于时空图注意力的短期电力负荷预测方法[J].湖南大学学报（自然科学版）,2024,51(2):57-67,11.基金项目
国家自然科学基金青年资助项目(62106072),National Natural Science Foundation of China(62106072) （62106072）
能源互联网供需运营湖南省重点实验室(2019TP1053),Hunan Provincial key Laboratory of Engrgy Internet Supply and Demand Operation(2019TP1053) （2019TP1053）
国网湖南省电力有限公司科技项目(5216A221N008),Hunan Electric Power Company Technology Projects(5216A221N008) （5216A221N008）
