On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
This work addresses grid edge intelligence for microgrid operators by enabling on-device forecasting, though it is incremental as it applies existing models to a new deployment scenario.
The paper tackled photovoltaic power forecasting in a microgrid by deploying graph neural networks (GCN and GraphSAGE) on edge smart meters, achieving successful execution with performance comparisons on both PC and meter hardware.
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.