Apr 23
On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
★★★★★
significance 2/5
This paper explores the application of graph neural networks, specifically GCN and GraphSAGE, for forecasting photovoltaic power generation on edge intelligent meters. The study details the development of a customized ONNX operator to facilitate model deployment and execution on smart meter hardware.
Why it matters
Deploying customized ONNX operators at the edge signals a shift toward localized, real-time intelligence for critical infrastructure management.
Tags
#graph neural networks #edge computing #photovoltaic forecasting #onnx #microgridRelated coverage
- Global South OpportunitiesPivotal Research Fellowship 2026 (Q3): AI Safety Research Opportunity - Global South Opportunities
- arXiv cs.AIAn Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement
- arXiv cs.AIPExA: Parallel Exploration Agent for Complex Text-to-SQL
- arXiv cs.AIThe Power of Power Law: Asymmetry Enables Compositional Reasoning
- arXiv cs.AIOn the Existence of an Inverse Solution for Preference-Based Reductions in Argumentation