6 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…
Diffusion-assisted Model Predictive Control Optimization for Power System Real-Time Operation
Linna Xu, Yongli Zhu
This paper presents a modified model predictive control (MPC) framework for real-time power system operation. The framework incorporates a diffusion model tailored for time series…
Generative Modeling and Data Augmentation for Power System Production Simulation
Linna Xu, Yongli Zhu
As a key component of power system production simulation, load forecasting is critical for the stable operation of power systems. Machine learning methods prevail in this field. Ho…
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…