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Arxiv
2d
106
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PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids
- The paper introduces a topology-aware Graph Neural Network (GNN) framework for predicting power system states in electricity grids with high renewable integration.
- The GNN model utilizes a graph-based representation of the power network, capturing both spatial and temporal correlations in system dynamics.
- It outperforms baseline approaches, achieving substantial improvements in predictive accuracy with average RMSEs of 0.13 to 0.17 across all predicted variables.
- The results highlight the potential of topology-aware learning for scalable and robust power system forecasting in future grids with high renewable penetration.
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