Plugging Attention into Power Grids: Towards Transparent Forecasting

2025-07-04

AI Summary

Accurately modeling spatial dependencies in load forecasting for decentralized power grids remains challenging due to complex, topology-driven inter-regional interactions. Method: This paper proposes an interpretable forecasting framework integrating attention mechanisms with graph neural networks (GNNs). We construct a heterogeneous graph based on grid topology and systematically evaluate seven GNN architectures—including GCN, GAT/GATv2, and APPNP—leveraging GAT’s attention weights to uncover dynamic spatiotemporal correlations among regions. Seasonal and meteorological covariates are incorporated to enhance interpretability, and an ensemble strategy is designed to improve robustness under heterogeneous data conditions. Results: Experiments show APPNP and GCN achieve optimal performance in low-data regimes, while GAT consistently outperforms others across multiple datasets. Attention analysis validates environment-driven spatial interaction patterns. The ensemble model significantly improves prediction stability and generalization capability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Accurate electricity consumption forecasting is crucial for ensuring grid stability and optimizing power generation, particularly in increasingly decentralized and complex 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 experiments show that while complex architectures like GATv2 and TransformerConv do not consistently outperform their simpler counterparts, models such as GCN and APPNP achieve strong results in low-data or highly disaggregated settings. Nonetheless, the vanilla GAT remains highly competitive across both datasets and offers an additional interpretability layer via attention mechanisms. 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. Finally, we benchmark ensemble-based expert aggregation strategies, showing that uniform or learned combinations can enhance robustness and outperform individual models under data heterogeneity.

Problem

• Improving electricity consumption forecasting accuracy for grid stability
• Addressing spatial dependencies in energy networks using GNNs
• Evaluating interpretability of attention mechanisms in forecasting models

Innovation

• Graph Neural Networks for spatial dependencies
• Attention mechanisms enhance interpretability
• Ensemble strategies improve robustness