On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
Abstract
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.
Cite
@article{arxiv.2604.19800,
title = {On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence},
author = {Jian Huang and Zixiang Ming and Yongli Zhu and Linna Xu},
journal= {arXiv preprint arXiv:2604.19800},
year = {2026}
}
Comments
This paper has been accepted for presentation at the 9th International Conference on Energy, Electrical and Power Engineering (CEEPE 2026) in Nanjing, China, April 17-19, 2026