中国电力2025，Vol.58Issue(3)：168-174,7.DOI:10.11930/j.issn.1004-9649.202406064
基于数据驱动时空网络的城市中长期电力负荷预测
Mid-long Term Urban Power Load Forecasting Based on Data-Driven Spatio-temporal Networks
摘要
Abstract
In order to ensure the quality of urban power grid planning and balance the power and electricity,accurate medium and long-term load forecasting becomes particularly.In view of the shortcomings of existing methods in utilizing the spatial correlation between urban areas,a prediction method based on dynamic time warping(DTW)and sp-temporal attention graph convolution(ASTGCN)is proposed.Firstly,the correlation between different regions in the target city is deeply analyzed to establish a coupling relationship.,the DTW algorithm is used to construct an adjacency matrix to capture the spatiotemporal correlation between different regions in the city.Then,the ASTGC model is applied to predict the load of each region to capture the spatiotemporal characteristics of the load.Finally,the overall urban prediction load is obtained by the prediction results of each region.The experimental results show that the proposed method can capture the spatiotemporal relationship in the city more comprehensively and significantly improve accuracy of medium and long-term load forecasting.关键词
中长期负荷预测/相关性分析/时空图卷积网络Key words
mid-long term load forecasting/correlation analysis/spatio-temporal graphical convolutional networks引用本文复制引用
孙庆超,李嘉靓,江万里,王若愚,李植鹏,胡亚荣,朱健斌..基于数据驱动时空网络的城市中长期电力负荷预测[J].中国电力,2025,58(3):168-174,7.基金项目
国家自然科学基金资助项目(62276068). This work is supported by National Natural Science Foundation of China(No.62276068). （62276068）
