Geospatial Mapping of Large-Scale Electric Power Grids: A Residual Graph Convolutional Network-Based Approach with Attention Mechanism

Precise geospatial mapping of grid infrastructure is essential for the effective development and administration of large-scale electrical infrastructure. The application of deep learning techniques in predicting regional energy network architecture utilizing extensive datasets of geographical information systems (GISs) has yet to be thoroughly investigated in previous research works. Moreover, although graph convolutional networks (GCNs) have been proven to be effective in capturing the complex linkages within graph-structured data, the computationally demanding nature of modern energy grids necessitates additional computational contributions. Hence, this research introduces a novel residual GCN with attention mechanism for mapping critical energy infrastructure components in geographic contexts. The proposed model accurately predicts the geographic locations and links of large-scale grid infrastructure, such as poles, electricity service points, and substations. The proposed framework is assessed on the Sultanate of Oman's regional energy grid and further validated on Nigeria's electricity transmission network database. The obtained findings showcase the model's capacity to accurately predict infrastructure components and their spatial relationships. Results show that the proposed method achieves a link-prediction accuracy of 95.88% for the Omani network and 92.98% for the Nigerian dataset. Furthermore, the proposed model achieved R2 values of 0.99 for both datasets in terms of regression. Therefore, the proposed architecture facilitates multifaceted assessment and enhances the capacity to capture the inherent geospatial aspects of large-scale energy distribution networks.