用图神经网络在智能电表上实现光伏功率预测，让边缘设备自主决策。
On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
- 在智能电表上部署GCN和GraphSAGE图模型，定制ONNX算子提升效率。
- 在村庄微电网真实数据上验证，模型在电表端成功运行且精度达标。
- 适合关注边缘计算与能源物联网的开发者和工程师参考。
本文详细研究了如何在微电网的边缘智能电表上利用图神经网络进行光伏功率预测。介绍了问题背景及采用的技术，包括ONNX与ONNX Runtime，简要描述了智能电表的软硬件配置。重点聚焦于两种图机器学习模型GCN与GraphSAGE的训练与部署，特别开发并集成了一种定制化的ONNX算子用于GCN。最后，基于某村庄微电网的真实数据开展案例研究，对比了两种模型在个人电脑与智能电表上的表现，结果显示模型在电表端成功部署并稳定执行。
原文摘要 · Abstract (English)
This paper presents a detailed study of how graph neural networks can be used on edge intelligent meters in a microgrid to forecast photovoltaic power generation. The problem background and the adopted technologies are introduced, including ONNX and ONNX Runtime. The hardware and software specifications of the smart meter are also briefly described. Then, the paper focuses on the training and deployment of two graph machine learning models, GCN and GraphSAGE, with particular emphasis on developing and deploying a customized ONNX operator for GCN. Finally, a case study is conducted using real datasets from a village microgrid. The performance of the two models is compared on both the PC and the smart meter, exhibiting successful deployments and executions on the smart meter.
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