Generative AI for Energy: Multi-Horizon Power Consumption Forecasting using Large Language Models
Year: 2024
Type: conference-paper
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.
Authors: Kevin Roitero, Gianluca D'Abrosca, Andrea Zancola, Vincenzo Della Mea, Stefano Mizzaro
Institution: University of Udine
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4403582608