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GPT-2-Augmented Sequence Modeling for Short-Term Load Forecasting
ID：77 View Protection：ATTENDEE Updated Time：2025-10-11 22:47:12 Hits：197 Poster Presentation

Start Time：2025-11-09 09:07（Asia/Shanghai）

Duration：1min

Session：P Poster presentation » P66.AI-driven technology

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Abstract
Abstract -- Load forecasting serves as the foundation for power system operation and planning. Accurate load forecasting ensures the secure and reliable operation of power systems, reduces generation costs, and enhances economic efficiency. Recent studies demonstrate that large language models (LLMs) exhibit powerful capabilities in pattern recognition and reasoning for complex token sequences. The critical challenge lies in effectively aligning temporal patterns in time-series data with linguistic structures in natural language to leverage these capabilities. This paper proposes a large model-based time-series forecasting method for electrical load prediction. The approach leverages a pre-trained GPT-2 (Generative Pre-trained Transformer 2) model as its foundation while freezing parameters in its self-attention and feed-forward neural network layers. Fine-tuning is applied exclusively to the input embedding layer and output projection layer. Experimental results demonstrate that the proposed method achieves performance comparable to or superior against existing approaches across multiple electrical load forecasting tasks.
 
Keywords
Load forecasting, time-series data, LLM, GPT-2, Fine-tuning
Speaker
Xu Kun
graduate student Southeast university

Submission Author
Xu Kun Southeast university
Wang Ying Southeast University
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Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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