GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting

2026-09-21

AI Summary

本文提出GraphToolbox，一个用于图神经网络预测的Python框架，通过统一数据驱动图构建、模型选择等阶段，简化电力预测中的空间信号处理问题。

Abstract

Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) provide a natural way to represent these relations. Building a complete GNN forecasting experiment is nonetheless laborious, because graph construction, model selection, training, aggregation, and interpretation sit in incompatible tools. We present GraphToolbox, an open-source Python framework that unifies these stages in one configurationdriven pipeline built on PyTorch Geometric. It offers data-driven graph construction, an adapter that instantiates and trains 51 of the 65 PyTorch Geometric convolutions together with the recurrent cells of PyTorch Geometric Temporal, online expert aggregation, forecasting interpretability, and significance testing on cached forecasts. We evaluate the pipeline in two case studies. On French regional load, the 48 convolutions included in the complete forecasting sweep fall in a band from 1.14% to 1.60% error, online aggregation lowers this to 0.98%, and the graph models improve on classical additive and boosting baselines. On net-load, direct graph models are less accurate than a classical additive model, while forecasting each physical component separately improves them without closing that gap. Both comparisons use the same experimental interface, illustrating the role of GraphToolbox in systematic architectural evaluation.

Problem

• Electricity forecasting
• Graph Neural Networks
• incompatible tools

Innovation

• Graph Neural Networks
• Forecasting Pipeline
• Configuration-Driven
• Online Aggregation
• Interpretability