Energy Demand Forecasting Using Deep Learning: Applications for the French Grid

This paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advan...

ver descrição completa

Detalhes bibliográficos
Autores: Real Torres, Alejandro del, Dorado, Fernando, Durán, Jaime
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/99506
Acesso em linha:https://hdl.handle.net/11441/99506
https://doi.org/10.3390/en13092242
Access Level:acceso abierto
Palavra-chave:Energy demand forecasting
Deep learning
Machine learning
Convolutional neural networks
Artificial neural networks
id ES_3733c07945f71fb5678ec35f4a5fe0c7
oai_identifier_str oai:idus.us.es:11441/99506
network_acronym_str ES
network_name_str España
repository_id_str
spelling Energy Demand Forecasting Using Deep Learning: Applications for the French GridReal Torres, Alejandro delDorado, FernandoDurán, JaimeEnergy demand forecastingDeep learningMachine learningConvolutional neural networksArtificial neural networksThis paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advantage of the virtues of both structures: the regression capabilities of the artificial neural network and the feature extraction capacities of the convolutional neural network. The proposed structure was trained and then used in a real setting to provide a French energy demand forecast using Action de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results show that this approach outperforms the reference Réseau de Transport d’Electricité (RTE, French transmission system operator) subscription-based service. Additionally, the proposed solution obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility of achieving high-accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.MDPIIngeniería de Sistemas y Automática2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/99506https://doi.org/10.3390/en13092242reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésEnergies, 13 (9), Article number 2242.https://doi.org/10.3390/en13092242info:eu-repo/semantics/openAccessoai:idus.us.es:11441/995062026-06-17T12:51:07Z
dc.title.none.fl_str_mv Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
title Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
spellingShingle Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
Real Torres, Alejandro del
Energy demand forecasting
Deep learning
Machine learning
Convolutional neural networks
Artificial neural networks
title_short Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
title_full Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
title_fullStr Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
title_full_unstemmed Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
title_sort Energy Demand Forecasting Using Deep Learning: Applications for the French Grid
dc.creator.none.fl_str_mv Real Torres, Alejandro del
Dorado, Fernando
Durán, Jaime
author Real Torres, Alejandro del
author_facet Real Torres, Alejandro del
Dorado, Fernando
Durán, Jaime
author_role author
author2 Dorado, Fernando
Durán, Jaime
author2_role author
author
dc.contributor.none.fl_str_mv Ingeniería de Sistemas y Automática
dc.subject.none.fl_str_mv Energy demand forecasting
Deep learning
Machine learning
Convolutional neural networks

Artificial neural networks
topic Energy demand forecasting
Deep learning
Machine learning
Convolutional neural networks
Artificial neural networks
description This paper investigates the use of deep learning techniques in order to perform energy demand forecasting. To this end, the authors propose a mixed architecture consisting of a convolutional neural network (CNN) coupled with an artificial neural network (ANN), with the main objective of taking advantage of the virtues of both structures: the regression capabilities of the artificial neural network and the feature extraction capacities of the convolutional neural network. The proposed structure was trained and then used in a real setting to provide a French energy demand forecast using Action de Recherche Petite Echelle Grande Echelle (ARPEGE) forecasting weather data. The results show that this approach outperforms the reference Réseau de Transport d’Electricité (RTE, French transmission system operator) subscription-based service. Additionally, the proposed solution obtains the highest performance score when compared with other alternatives, including Autoregressive Integrated Moving Average (ARIMA) and traditional ANN models. This opens up the possibility of achieving high-accuracy forecasting using widely accessible deep learning techniques through open-source machine learning platforms.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/99506
https://doi.org/10.3390/en13092242
url https://hdl.handle.net/11441/99506
https://doi.org/10.3390/en13092242
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Energies, 13 (9), Article number 2242.
https://doi.org/10.3390/en13092242
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406016203915265
score 15,198674

Registos relacionados