machine learning lightness; power consumption; forecasting; transformer
Résumé :
[en] Energy demand forecasting is one of the most challenging tasks for grids operators.
Many approaches have been suggested over the years to tackle it. Yet, those still remain too expensive to train in terms of both time and computational resources, hindering their adoption as customers behaviors are continuously evolving.
We introduce Transplit, a new lightweight transformer-based model, which significantly decreases this cost by exploiting the seasonality property and learning typical days of power demand. We show that Transplit can be run efficiently on CPU and is several hundred times faster than state-of-the-art predictive models, while performing as well.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Disciplines :
Sciences informatiques
Auteur, co-auteur :
BERNIER, Fabien ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
JIMENEZ, Matthieu ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Computer Science (DCS)
CORDY, Maxime ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
LE TRAON, Yves ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SerVal
Co-auteurs externes :
no
Langue du document :
Anglais
Titre :
Faster and Cheaper Energy Demand Forecasting at Scale
Date de publication/diffusion :
02 décembre 2022
Nom de la manifestation :
Has it Trained Yet? Workshop at the Conference on Neural Information Processing Systems
Date de la manifestation :
02-12-2022
Manifestation à portée :
International
Titre de l'ouvrage principal :
Has it Trained Yet? Workshop at the Conference on Neural Information Processing Systems
