Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks

2026-02-24

AI Summary

This study addresses the challenge of accurately matching multi-scale spatial models in energy system coupling, where existing approaches rely on a single geographic attribute to generate aggregation weights, thereby limiting both accuracy and physical plausibility. To overcome this limitation, we propose a novel framework based on a self-supervised heterogeneous graph neural network that represents high-resolution geographic units as nodes, integrates multi-source geographic features, and leverages Voronoi diagrams for spatial clustering. Our method enables, for the first time, physically interpretable weight learning without requiring ground-truth labels. By moving beyond the constraints of single-attribute assumptions, the approach significantly enhances the accuracy, scalability, and physical consistency of spatial disaggregation, outperforming current state-of-the-art techniques.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Graph-based Machine Learning

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: Models for Web evolution

Abstract

In energy system analysis, coupling models with mismatched spatial resolutions is a significant challenge. A common solution is assigning weights to high-resolution geographic units for aggregation, but traditional models are limited by using only a single geospatial attribute. This paper presents an innovative method employing a self-supervised Heterogeneous Graph Neural Network to address this issue. This method models high-resolution geographic units as graph nodes, integrating various geographical features to generate physically meaningful weights for each grid point. These weights enhance the conventional Voronoi-based allocation method, allowing it to go beyond simply geographic proximity by incorporating essential geographic information.In addition, the self-supervised learning paradigm overcomes the lack of accurate ground-truth data. Experimental results demonstrate that applying weights generated by this method to cluster-based Voronoi Diagrams significantly enhances scalability, accuracy, and physical plausibility, while increasing precision compared to traditional methods.

Problem

• spatial allocation
• energy system coupling
• graph neural networks
• geospatial attributes
• resolution mismatch

Innovation

• Graph Neural Networks
• Spatial Allocation
• Energy System Coupling
• Self-supervised Learning
• Heterogeneous Graph