Reference : Faster and Cheaper Energy Demand Forecasting at Scale

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	Focus Areas :	Computational Sciences
	To cite this reference:	http://hdl.handle.net/10993/53152

Title :	Faster and Cheaper Energy Demand Forecasting at Scale
Language :	English
Author, co-author :	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 >]
Publication date :	2-Dec-2022
Main document title :	Has it Trained Yet? Workshop at the Conference on Neural Information Processing Systems
Peer reviewed :	Yes
Audience :	International
Event name :	Has it Trained Yet? Workshop at the Conference on Neural Information Processing Systems
Event date :	02-12-2022
Keywords :	[en] machine learning lightness ; power consumption ; forecasting ; transformer
Abstract :	[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.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Security Design and Validation Research Group (SerVal)
Name of the research project :	Secure, reliable and predictable smart grid
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/53152

	File(s) associated to this reference
	Fulltext file(s): File Commentary Version Size Access Open access HITY-Transplit.pdf Author preprint 446.27 kB View/Open

All documents in ORBi^lu are protected by a user license.

 

University of Luxembourg Library |  Feedback |  Legal notices Site Map