2024/12/02 by Linna Xu, Jian Huang, Xu, Linna +5
Engineering · Computer Science · #Smart Grid Energy Management #Smart Grid Security and Resilience #IoT and Edge/Fog Computing
paper · pdf · doi:10.48550/arxiv.2412.01080
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 and control algorithms are directly deployed on an edge-computing device (a smart meter-concentrator) in the microgrid rather than on a cloud server at the far-end control center, reducing the communication time the inverters need to wait. Experimental results on an ARM-based smart meter board demonstrate the feasibility and correctness of the proposed approach by comparing against the results on the desktop PC.