用图神经网络提升能源系统空间耦合的精度与合理性。
Improving Spatial Allocation for Energy System Coupling with Graph Neural Networks
- 构建异质图模型，融合多维地理特征生成权重
- 相比传统方法，精度、可扩展性与物理合理性显著提升
- 自监督学习无需真实标签，适合数据稀缺场景
在能源系统分析中，不同空间分辨率的耦合模型面临挑战。传统方法仅依赖单一地理属性赋权，难以反映复杂空间关系。本文提出一种自监督异质图神经网络方法，将高分辨率地理单元建模为图节点，整合多种地理特征生成具有物理意义的网格点权重。该权重改进了基于聚类的Voronoi图分配方式，突破单纯依赖地理邻近性的局限。自监督学习机制有效缓解真实标签缺失问题。实验表明，应用该方法生成的权重显著提升了模型的可扩展性、准确性和物理合理性，精度优于传统方法。
原文摘要 · Abstract (English)
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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