This research introduces a novel time-based large language model approach that significantly improves distributed photovoltaic power forecasting accuracy while reducing dependency on expensive external data.

• Leverages temporal relationship modeling to capture complex patterns in solar power generation
• Achieves superior forecasting performance with minimal data requirements
• Demonstrates practical engineering solutions for renewable energy integration challenges
• Enhances power grid reliability by better predicting intermittent solar energy production

For power system engineers, this advancement means more efficient grid operations, reduced energy losses, and improved planning capabilities for distributed renewable resources.

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