dc.creator | Lara Benítez, Pedro | es |
dc.creator | Carranza García, Manuel | es |
dc.creator | Luna Romera, José María | es |
dc.creator | Riquelme Santos, José Cristóbal | es |
dc.date.accessioned | 2020-06-14T08:56:19Z | |
dc.date.available | 2020-06-14T08:56:19Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Lara Benítez, P., Carranza García, M., Luna Romera, J.M. y Riquelme Santos, J.C. (2020). Temporal convolutional networks applied to energy-related time series forecasting. Applied Sciences, 10 (7) | |
dc.identifier.issn | 2076-3417 | es |
dc.identifier.uri | https://hdl.handle.net/11441/97777 | |
dc.description.abstract | Modern energy systems collect high volumes of data that can provide valuable information
about energy consumption. Electric companies can now use historical data to make informed
decisions on energy production by forecasting the expected demand. Many deep learning models
have been proposed to deal with these types of time series forecasting problems. Deep neural
networks, such as recurrent or convolutional, can automatically capture complex patterns in time
series data and provide accurate predictions. In particular, Temporal Convolutional Networks
(TCN) are a specialised architecture that has advantages over recurrent networks for forecasting
tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual
blocks, and can also be more efficient in terms of computation time. In this work, we propose a
TCN-based deep learning model to improve the predictive performance in energy demand forecasting.
Two energy-related time series with data from Spain have been studied: the national electric demand
and the power demand at charging stations for electric vehicles. An extensive experimental study has
been conducted, involving more than 1900 models with different architectures and parametrisations.
The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM)
recurrent networks, which are considered the state-of-the-art in the field. | es |
dc.description.sponsorship | Ministerio de Economía y Competitividad TIN2017-88209-C2-2-R | es |
dc.description.sponsorship | Junta de Andalucía US-1263341 | es |
dc.description.sponsorship | Junta de Andalucía P18-RT-2778 | es |
dc.format | application/pdf | es |
dc.format.extent | 17 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Applied Sciences, 10 (7) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep learning | es |
dc.subject | Energy demand | es |
dc.subject | Temporal convolutional network | es |
dc.subject | Time series forecasting | es |
dc.title | Temporal convolutional networks applied to energy-related time series forecasting | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2017-88209-C2-2-R | es |
dc.relation.projectID | US-1263341 | es |
dc.relation.projectID | P18-RT-2778 | es |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/10/7/2322 | es |
dc.identifier.doi | 10.3390/app10072322 | es |
dc.journaltitle | Applied Sciences | es |
dc.publication.volumen | 10 | es |
dc.publication.issue | 7 | es |
dc.contributor.funder | Ministerio de Economía y Competitividad (MINECO). España | es |
dc.contributor.funder | Junta de Andalucía | es |
dc.contributor.funder | Junta de Andalucía | es |