Correlated load forecasting in active distribution networks using Spatial-Temporal Synchronous Graph Convolutional Networks

被引:8
|
作者
Yu, Qun [1 ]
Li, Zhiyi [1 ]
机构
[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 310027, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
24;
D O I
10.1049/esi2.12028
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Load forecasting becomes increasingly challenging as power distribution networks evolve towards active distribution networks with high-penetration renewables. In the context of active distribution networks, the load can be principally referred to as a mixture of power consumption devices as well as renewables-based distributed energy resources behind the meters. Accordingly, more hidden information (e.g., correlations) should be mined from historical load observations to relieve the significant challenges resulting from behind-the-meter renewables. Here, a novel spatial-temporal graph representation method is proposed to characterise and present spatial and temporal correlations of historical load observations. The graph-structured data is then fed into a model denoted as Spatial-Temporal Synchronous Graph Convolutional Network (STSGCN) for performing load forecasting by extracting the inherent spatial-temporal features of historical load observations. Finally, numerical experiments are performed on a real-world load dataset. The results show that the proposed method manages to capture spatial-temporal correlations of load observations in the forecasting process while outperforming the state of the art in terms of overall forecasting accuracy.
引用
收藏
页码:355 / 366
页数:12
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