  Accurate load forecasting is essential for maintaining the power balance
between generators and consumers, especially with the increasing integration of
renewable energy sources, which introduce significant intermittent volatility.
With the development of data-driven methods, machine learning and deep
learning-based models have become the predominant approach for load forecasting
tasks. In recent years, pre-trained language models (PLMs) have made
significant advancements, demonstrating superior performance in various fields.
This paper proposes a load forecasting method based on PLMs, which offers not
only accurate predictive ability but also general and flexible applicability.
Additionally, a data modeling method is proposed to effectively transform load
sequence data into natural language for PLM training. Furthermore, we introduce
a data enhancement strategy that eliminate the impact of PLM hallucinations on
forecasting results. The effectiveness of the proposed method has been
validated on two real-world datasets. Compared with existing methods, our
approach shows state-of-the-art performance across all validation metrics.
