Mid-long Term Urban Power Load Forecasting Based on Data-Driven Spatio-temporal Networks

doi:10.11930/j.issn.1004-9649.202406064

Abstract

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.

Key words: mid-long term load forecasting, correlation analysis, spatio-temporal graphical convolutional networks

Qingchao SUN, Jialiang LI, Wanli JIANG, Ruoyu WANG, Zhipeng LI, Yarong HU, Jianbin ZHU. Mid-long Term Urban Power Load Forecasting Based on Data-Driven Spatio-temporal Networks[J]. Electric Power, 2025, 58(3): 168-174.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202406064

https://www.electricpower.com.cn/EN/Y2025/V58/I3/168

Figures/Tables 8

Fig.1 Structure of spatio-temporal graph convolution

Fig.2 DTW-ASTGCN prediction model

Fig.3 Power load curves in different regions

Fig.4 Comparison of prediction curves of different correlation measurement methods

Fig.5 Comparison of prediction curves of different models

Fig.6 Prediction error MAE comparison in different regions

Fig.7 Prediction error RMSE comparison in different regions

Fig.8 Prediction error MAPE comparison in different regions

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