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

Year: 2026

Type: article

Abstract: 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.

Source: EAI Endorsed Transactions on Energy Web

DOI: 10.4108/ew.12109

Authors: Hongxiang Cai, Zhiyong Wang, Yangping Tang, Jin Ao, Qian Li

Institutions: State Grid Zhejiang Electric Power Company (China), State Grid Corporation of China (China)

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: diamond

OpenAlex ID: W7154453262

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