Prompt-TSF: A Power Load Forecasting Model Based on Prompt Learning and Two-Stage Fine-Tuning

Accurate power load forecasting is vital for grid stability. Traditional models struggle with complex nonlinearities, while adapting Large Language Models (LLMs) to time-series data faces modal alignment challenges. This paper proposes Prompt-TSF, a framework employing prompt learning and two-stage fine-tuning for multivariate forecasting. We utilize a high-frequency dataset of load and weather variables from Quanzhou, China. A custom tool transforms numerical data into natural language prompts, bridging the modal gap and enriching context. Furthermore, a unique two-stage fine-tuning strategy trains the LLM first on domain knowledge and formatting, followed by specific predictive regression. Experiments demonstrate that Prompt-TSF significantly outperforms traditional forecasting methods.