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This paper presents AcegasApsAmga’s application of Large Language Models (LLMs) for energy forecasting, focusing on both short-term and long-term power consumption predictions. We detail the model adaptation process, including fine-tuning techniques specific to energy data, and the integration of temporal and contextual features using Retrieval Augmented Generation (RAG) to enhance forecasting accuracy.

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Leveraging LLMs for Energy Forecasting: The AcegasApsAmga Case Study

  • 会议论文

  • 发表日期：

    2025年

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  • Kevin Roitero,
  • Andrea Zancola,
  • Vincenzo Della Mea,
  • Stefano Mizzaro

摘要

This paper presents AcegasApsAmga’s application of Large Language Models (LLMs) for energy forecasting, focusing on both short-term and long-term power consumption predictions. We detail the model adaptation process, including fine-tuning techniques specific to energy data, and the integration of temporal and contextual features using Retrieval Augmented Generation (RAG) to enhance forecasting accuracy.