# Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks

arXiv: https://arxiv.org/abs/2602.22249 · HTML: https://mlsanity.com/paper/2602.22249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 24 Feb 2026.

## Builds on (most cited first, 4 of 4)

- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Modeling and Analyzing Urban Networks and Amenities with OSMnx](https://mlsanity.com/paper/2505.00736/) (2025)
- [Datasets of Great Britain Primary Substations Integrated with Household Heating Information](https://mlsanity.com/paper/2403.16313/) (2024)
- [On Clustering Induced Voronoi Diagrams](https://mlsanity.com/paper/2404.18906/) (2024)

## Abstract (arXiv)

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.