Correlated load forecasting in active distribution networks using Spatial‐Temporal Synchronous Graph Convolutional Networks. Issue 3 (14th June 2021)

• Record Type: Journal Article
• Authors: Yu, Qun; Li, Zhiyi
• Other Names: Jiang Tao guestEditor.; Bai Linquan guestEditor.; Mu Yunfei guestEditor.; Venayagamoorthy Kumar guestEditor.; Zhang Yingchen guestEditor.; Teng Fei guestEditor.; Chen Peiyuan guestEditor.; Zhong Haiwang guestEditor.; Yao Wei guestEditor.; Wan Can guestEditor.
• Is Part Of: IET energy systems integration. Volume 3:Issue 3(2021)
• Page Start: 355
• Page End: 366
• Publication Date: 2021-06-14
• Subjects: Power resources -- Periodicals; Energy conservation -- Periodicals; Power resources; Energy conservation; Periodicals; 333.79
• DOI: 10.1049/esi2.12028
• Languages: English
• ISSNs: 2516-8401

Abstract

Abstract: 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.