4 papers
On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
Jian Huang, Zixiang Ming, Yongli Zhu +1
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 backg…
On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence
Jian Huang, Yongli Zhu, Linna Xu +3
In this paper, an edge-side model training study is conducted on a resource-limited smart meter. The motivation of grid-edge intelligence and the concept of on-device training are…
Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters
Linna Xu, Jian Huang, Shan Yang +1
In this paper, an edge computing-based machine-learning study is conducted for solar inverter power forecasting and droop control in a remote microgrid. The machine learning models…
Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid
Yongli Zhu, Linna Xu, Jian Huang
This paper presents a machine-learning study for solar inverter power regulation in a remote microgrid. Machine learning models for active and reactive power control are respective…