On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence

This paper details the use of graph neural networks (GNNs) for photovoltaic power forecasting on edge intelligent meters in a microgrid. It explores the training and deployment of GCN and GraphSAGE models, including a customized ONNX operator, with a real-world case study demonstrating successful execution on smart meters.