# Reliable Communication in Distributed Photovoltaic Sensor Networks: A Large Language Model-Driven Approach

## TL;DR

This paper introduces a new method using large language models to improve communication and diagnostics in solar energy systems.

## Contribution

A novel hierarchical framework combining traffic shaping and LLMs for efficient fault diagnosis in DPV systems.

## Key findings

- The method reduces P50 latency by 46.08% to 49.87% under 10 Mbps gateway bandwidth.
- LLM-powered diagnostics enable precise fault diagnosis without requiring training data.

## Abstract

Distributed photovoltaic (DPV) systems present a cost-effective and sustainable industrial energy solution, yet their reliable monitoring faces significant technological constraints. This paper proposes a hierarchical optimization framework that integrates hysteresis-based traffic shaping at the network layer with Large Language Model (LLM)-driven diagnostics at the application layer. The proposed dynamic algorithm minimizes latency and downtime by prioritizing critical fault data. Priority-based scheduling ensures this critical data is transmitted preferentially over routine sensor readings. At the application layer, the system utilizes physics-informed prompt engineering to perform zero-shot root cause analysis, circumventing the training data requirements of traditional classifiers. Under a 10 Mbps gateway bandwidth, our method achieves a 46.08% to 49.87% reduction in P50 latency compared to traditional approaches. Moreover, the LLM-powered diagnostic system provides detailed assessments, enabling precise fault diagnosis for DPV systems.