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.
Leveraging LLMs for Energy Forecasting: The AcegasApsAmga Case Study
Roitero K.;Della Mea V.;Mizzaro S.
2025-01-01
Abstract
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.File in questo prodotto:
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