A Method for Load Forecasting of Distribution Transformers Based on Parameter-Efficient Fine-Tuning of Large Language Models in Power System

Distribution grids are essential to power systems, as they ensure the safe and reliable operation of power systems. It is important to forecast the operational status of each distribution transformer. Consequently, there is an urgent need to develop load forecasting models that exhibit robust generalization and exceptional accuracy. However, distribution transformers are numerous, exhibit strong inherent features. Single-device load samples are scarce, with widespread few-shot issues. Traditional models rely on massive amounts of data and computational resources, making it challenging to balance computational costs, generalization ability, and accuracy. To address these issues, this paper analyzes the inherent features of distribution transformers and fine-tunes large language models using parameter-efficient fine-tuning methods to reduce training costs while preserving their generalization capabilities and accuracy. Experimental results demonstrate that this method achieves significantly higher accuracy than baseline models in few-shot scenarios.