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

TL;DR

This paper proposes an embedded machine learning approach for solar inverter power regulation in remote microgrids, enabling real-time control with low latency by deploying models on edge devices instead of central servers.

Contribution

It introduces a novel deployment of ensemble learning models on embedded hardware for solar power regulation, reducing communication delays in remote microgrids.

Findings

Embedded models match desktop performance with 0.1ms inference time.

Deployment on edge devices reduces communication delays.

Real-world experiments validate the approach's effectiveness.

Abstract

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.