A General Framework for Load Forecasting based on Pre-trained Large Language Model

TL;DR

This paper introduces a novel load forecasting framework utilizing pre-trained large language models, transforming load data into natural language and employing data enhancement to improve accuracy and robustness, validated on real datasets.

Contribution

It presents a new load forecasting approach based on LLMs, including data transformation and enhancement techniques, achieving state-of-the-art results.

Findings

Outperforms existing load forecasting methods on real datasets

Effective data transformation from load sequences to natural language

Data enhancement reduces LLM hallucination impacts

Abstract

Accurate load forecasting is crucial for maintaining the power balance between generators and consumers,particularly with the increasing integration of renewable energy sources, which introduce significant intermittent volatility. With the advancement of data-driven methods, machine learning and deep learning models have become the predominant approaches for load forecasting tasks. In recent years, pre-trained large language models (LLMs) have achieved significant progress, demonstrating superior performance across various fields. This paper proposes a load forecasting method based on LLMs, offering not only precise predictive capabilities but also broad and flexible applicability. Additionally, a data modeling method is introduced to effectively transform load sequence data into natural language suitable for LLM training. Furthermore, a data enhancement strategy is designed to mitigate the impact of LLM hallucinations on forecasting results. The effectiveness of the proposed method is validated using two real-world datasets. Compared to existing methods, our approach demonstrates state-of-the-art performance across all validation metrics.