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

arXiv: https://arxiv.org/abs/2412.01080 · HTML: https://mlsanity.com/paper/2412.01080/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence](https://mlsanity.com/paper/2604.19800/) (2026)

## Abstract (arXiv)

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.