# A Novel Distributed PV Power Forecasting Approach Based on Time-LLM

arXiv: https://arxiv.org/abs/2503.06216 · HTML: https://mlsanity.com/paper/2503.06216/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2025.

## Built on by (most cited first, 3 of 3)

- [PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses](https://mlsanity.com/paper/2508.17094/) (2025)
- [Cloud-Edge Collaborative Large Models for Robust Photovoltaic Power Forecasting](https://mlsanity.com/paper/2603.22343/) (2026)
- [Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models](https://mlsanity.com/paper/2609.11135/) (2026)

## Abstract (arXiv)

Distributed photovoltaic (DPV) systems are essential for advancing renewable energy applications and achieving energy independence. Accurate DPV power forecasting can optimize power system planning and scheduling while significantly reducing energy loss, thus enhancing overall system efficiency and reliability. However, solar energy's intermittent nature and DPV systems' spatial distribution create significant forecasting challenges. Traditional methods often rely on costly external data, such as numerical weather prediction (NWP) and satellite images, which are difficult to scale for smaller DPV systems. To tackle this issue, this study has introduced an advanced large language model (LLM)-based time series forecasting framework Time-LLM to improve the DPV power forecasting accuracy and generalization ability. By reprogramming, the framework aligns historical power data with natural language modalities, facilitating efficient modeling of time-series data. Then Qwen2.5-3B model is integrated as the backbone LLM to process input data by leveraging its pattern recognition and inference abilities, achieving a balance between efficiency and performance. Finally, by using a flatten and linear projection layer, the LLM's high-dimensional output is transformed into the final forecasts. Experimental results indicate that Time-LLM outperforms leading recent advanced time series forecasting models, such as Transformer-based methods and MLP-based models, achieving superior accuracy in both short-term and long-term forecasting. Time-LLM also demonstrates exceptional adaptability in few-shot and zero-shot learning scenarios. To the best of the authors' knowledge, this study is the first attempt to explore the application of LLMs to DPV power forecasting, which can offer a scalable solution that eliminates reliance on costly external data sources and improve real-world forecasting accuracy.