Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911527704985600 |
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| author | Mu, Xuanhao Geiges, Jakob Liu, Nan Schlachter, Thorsten Hagenmeyer, Veit |
| author_facet | Mu, Xuanhao Geiges, Jakob Liu, Nan Schlachter, Thorsten Hagenmeyer, Veit |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_22249 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks Mu, Xuanhao Geiges, Jakob Liu, Nan Schlachter, Thorsten Hagenmeyer, Veit Machine Learning Systems and Control 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. |
| title | Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2602.22249 |