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Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks

Xuanhao Mu, Jakob Geiges, Nan Liu, Thorsten Schlachter, Veit Hagenmeyer

TL;DR

Experimental results demonstrate that applying weights generated by this self-supervised Heterogeneous Graph Neural Network to cluster-based Voronoi Diagrams significantly enhances scalability, accuracy, and physical plausibility, while increasing precision compared to traditional methods.

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.

Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks

TL;DR

Experimental results demonstrate that applying weights generated by this self-supervised Heterogeneous Graph Neural Network to cluster-based Voronoi Diagrams significantly enhances scalability, accuracy, and physical plausibility, while increasing precision compared to traditional methods.

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.
Paper Structure (14 sections, 5 equations, 3 figures, 1 table)

This paper contains 14 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related work
  3. Method
  4. Graph Builder
  5. Graph Encoder
  6. Edge Weight Predictor
  7. Loss Function
  8. Clustering Introduced Voronoi Diagram
  9. Results
  10. Allocation Performance
  11. Physical Plausibility
  12. Discussion
  13. Conclusion
  14. Declaration

Figures (3)

  • Figure 1: Flowchart of the GNN-based spatial allocation method. The main pipeline (blue) generates weights, while the self-supervised loop (red) computes the loss for training by reconstructing macro-indicators.
  • Figure 2: Land Use Distribution with Substation in Region TLD4
  • Figure 3: Land Use Distribution and Weights Heat Map for region London (top) and TLC1 (bottom)