arXiv cs.AI· Eloi Campagne (CB), Yvenn Amara-Ouali (LMO), Yannig Goude (LMO), Itai Zehavi (CB), Argyris Kalogeratos (CB)·· 2 小时前AI 评分40
电力负荷预测中 GNN 的诊断协议：区分参数共享与邻域访问
A Diagnostic Protocol for GNNs in Electricity Load Forecasting: Separating Parameter Sharing from Neighborhood Access
AI 导读
arXiv 论文提出一种诊断协议，将 GNN 与三个参照模型对比，以区分负荷预测收益来自跨序列参数共享还是邻域访问，并用保度重连检验邻居身份是否重要。在法国区域负荷与英国低压馈线数据上，参数共享解释了 GNN 相对节点级模型误差差距的大部分（对正则化模型约三分之二），边的增益较小且重连后消失一半以上；三个数据集中边的贡献均未占主导，作者建议只有与节点级和共享参照模型对比后才能把收益归功于图结构。
正文 · 原文
Abstract:A graph neural network (GNN) forecasting a collection of load series combines two mechanisms, sharing parameters across series and reading neighboring series, and a gain over a non-graph model may come from either; telling them apart decides whether a graph is worth maintaining. We propose a model diagnostic protocol that compares the GNN with three reference models: a per-node network, a network shared across nodes without neighbors, and the same GNN without edges. Degree-preserving rewiring tests whether neighbor identity matters. After a tuned benchmark on regional load in France and on low-voltage feeders in the UK, we apply the protocol to deliberately short node histories, as when non-stationary demand leaves only recent observations relevant: the feeders, within one week, and a public dataset of client consumption series. On the feeders, sharing parameters recovers most of the error gap between node-specific models and GNNs, about two thirds against a regularized one. Adding the edges to an otherwise identical backbone brings a smaller gain, half or more of which disappears under rewiring. On the client series, the neighbor mean degrades the per-node model on the shorter window and the edges' gain is not consistently distinguishable from zero; on the regions of France, where each node's own history is already informative, the ranking between per-node models and GNNs changes with tuning. A synthetic study points to noisy local measurements as one regime where neighbors help. In an exploratory online aggregation, graph and non-graph forecasters appear complementary, and the weight assigned to GNNs is not proportional to their contribution. In none of the three datasets do the edges account for most of a GNN's advantage over node-specific models, so a forecasting gain should be credited to the graph only after comparison with per-node and shared references.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.35074 [cs.AI] |
| (or arXiv:2610.35074v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35074 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Eloi Campagne [view email] [via CCSD proxy]
[v1]
Mon, 26 Oct 2026 09:20:23 UTC (2,015 KB)
来源：arXiv cs.AI · arxiv.org