Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks

• URL: http://arxiv.org/abs/2602.22249v1
• Date: Tue, 24 Feb 2026 19:53:45 GMT
• Title: Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks
• Authors: Xuanhao Mu, Jakob Geiges, Nan Liu, Thorsten Schlachter, Veit Hagenmeyer,
• Abstract summary: This paper presents an innovative method employing a self-supervised Heterogeneous Graph Neural Network to address this issue.<n>This method models high-resolution geographic units as graph nodes, integrating various geographical features to generate physically meaningful weights for each grid point.<n> Experimental results demonstrate that applying weights generated by this method to cluster-based Voronoi Diagrams significantly enhances scalability, accuracy, and physical plausibility.
• Score: 1.9140324483646127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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