Generative AI for Energy: Multi-Horizon Power Consumption Forecasting using Large Language Models

We leverage generative NLP-based models, specifically Transformer-Based models, for multi-horizon univariate and multivariate power consumption forecasting. We apply our approach to various datasets, focusing on short-term (1 day) and long-term (1 week) forecasts. We test several lag configurations with and without additional contextual information and achieve promising results. We evaluate the forecasts' effectiveness using a range of metrics, and aggregate the results on a monthly basis for a comprehensive understanding of the performance throughout the year.

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Publication Details

Published
2024-10-20
DOI
https://doi.org/10.1145/3627673.3679933
Citations
2
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.42
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article

Generative AI for Energy: Multi-Horizon Power Consumption Forecasting using Large Language Models

Stefano Mizzaro, Vincenzo Della Mea, Kevin Roitero, Gianluca D'Abrosca et al.
2 citations
Energy Load and Power Forecasting
0.42
article

Generative AI for Energy: Multi-Horizon Power Consumption Forecasting using Large Language Models

Stefano Mizzaro, Vincenzo Della Mea, Kevin Roitero, Gianluca D'Abrosca, Andrea Zancola
article en
2 citations

Abstract

We leverage generative NLP-based models, specifically Transformer-Based models, for multi-horizon univariate and multivariate power consumption forecasting. We apply our approach to various datasets, focusing on short-term (1 day) and long-term (1 week) forecasts. We test several lag configurations with and without additional contextual information and achieve promising results. We evaluate the forecasts' effectiveness using a range of metrics, and aggregate the results on a monthly basis for a comprehensive understanding of the performance throughout the year.

University of Udine (IT)
Affordable and clean energy
Openalex Percentile: Top 37%
Energy Load and Power Forecasting
0.42
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