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arXiv · 2412.01080

Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters

Linna Xu·Jian Huang·Shan Yang·Yongli Zhu
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Abstract

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.

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BibTeXRIS

Linna Xu, Jian Huang, Shan Yang, Yongli Zhu. 2024-12-02. Edge Computing for Microgrid via MATLAB Embedded Coder and Low-Cost Smart Meters. https://arxiv.org/abs/2412.01080

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