📝 SummarXiv

Abstract

Reliable prediction of electricity demand plays a key role in safeguarding grid stability and guiding generation decisions, a need that grows with the decentralization and complexity of modern systems. While classical approaches such as Generalized Additive Models (GAMs) remain widely used, they often fail to capture the spatial dependencies inherent in energy networks. Graph Neural Networks (GNNs) offer a principled framework to incorporate this structure by directly leveraging graph topologies. In this work, we evaluate a broad set of GNN architectures -- including GCN, GraphSAGE, ChebConv, TAG, APPNP, TransformerConv, and Graph Attention Networks (GAT and GATv2) -- on two real-world electricity consumption datasets from France and the UK. Our results show that simpler models such as GCN, SAGE, or APPNP often outperform more complex alternatives in low-data regimes, while GAT ranks among the strongest architectures in our benchmarks, combining high accuracy with valuable interpretability. We perform a temporal analysis of attention weights, revealing evolving patterns of regional interaction linked to seasonal and meteorological variability. These results highlight that, although attention is not universally superior, it provides valuable explanatory power when spatial dependencies are prominent. Additionally, we demonstrate that ensemble-based expert aggregation strategies, particularly bottom-up combinations, significantly improve robustness and yield state-of-the-art performance across both datasets. These findings highlight the dual promise of GNNs for accurate and interpretable forecasting, and suggest that architectural simplicity coupled with ensemble methods can provide a practical path forward for transparent energy analytics.