# Embedded Machine Learning for Solar PV Power Regulation in a Remote Microgrid

arXiv: https://arxiv.org/abs/2412.01054 · HTML: https://mlsanity.com/paper/2412.01054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 10 references that are in the map.
- First posted on arXiv 2 Dec 2024.

## Builds on (most cited first, 1 of 1)

- [Flexible and Fully Quantized Ultra-Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems](https://mlsanity.com/paper/2307.05999/) (2023)

## Built on by (most cited first, 2 of 2)

- [On-Device Training of PV Power Forecasting Models in a Smart Meter for Grid Edge Intelligence](https://mlsanity.com/paper/2507.07016/) (2025)
- [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)

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 respectively trained using an ensemble learning method. Then, unlike conventional schemes that make inferences on a central server in the far-end control center, the proposed scheme deploys the trained models on an embedded edge-computing device near the inverter to reduce the communication delay. Experiments on a real embedded device achieve matched results as on the desktop PC, with about 0.1ms time cost for each inference input.