M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments

[EN] We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which make...

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Detalles Bibliográficos
Autores: Paz Centeno, Iván de, García Ordás, María Teresa, García-Olalla Olivera, Óscar, Arenas Díez, Javier, Alaiz Moretón, Héctor
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/21277
Acceso en línea:https://www.mdpi.com/1996-1073/14/16/4765
https://hdl.handle.net/10612/21277
Access Level:acceso abierto
Palabra clave:Energía
Ingeniería de sistemas
Super resolution perception
Super resolution of energy
Data interpolation
Convolutional neural network
Deep-learning
3322.04 Transmisión de Energía
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oai_identifier_str oai:buleria.unileon.es:10612/21277
network_acronym_str ES
network_name_str España
repository_id_str
spelling M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption EnvironmentsPaz Centeno, Iván deGarcía Ordás, María TeresaGarcía-Olalla Olivera, ÓscarArenas Díez, JavierAlaiz Moretón, HéctorEnergíaIngeniería de sistemasSuper resolution perceptionSuper resolution of energyData interpolationConvolutional neural networkDeep-learning3322.04 Transmisión de Energía[EN] We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which makes it suitable to effectively replace energy apportioning systems. Experiments demonstrate that M-SRPCNN can effectively reconstruct load curves from single month overall values, outperforming traditional apportioning systems.SIMinisterio de Ciencia e Innovación ( DIN2018-009733)MDPIIngenieria de Sistemas y AutomaticaEscuela de Ingenierias Industrial, Informática y Aeroespacial2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://www.mdpi.com/1996-1073/14/16/4765https://hdl.handle.net/10612/21277reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/212772026-06-24T12:43:27Z
dc.title.none.fl_str_mv M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
title M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
spellingShingle M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
Paz Centeno, Iván de
Energía
Ingeniería de sistemas
Super resolution perception
Super resolution of energy
Data interpolation
Convolutional neural network
Deep-learning
3322.04 Transmisión de Energía
title_short M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
title_full M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
title_fullStr M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
title_full_unstemmed M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
title_sort M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments
dc.creator.none.fl_str_mv Paz Centeno, Iván de
García Ordás, María Teresa
García-Olalla Olivera, Óscar
Arenas Díez, Javier
Alaiz Moretón, Héctor
author Paz Centeno, Iván de
author_facet Paz Centeno, Iván de
García Ordás, María Teresa
García-Olalla Olivera, Óscar
Arenas Díez, Javier
Alaiz Moretón, Héctor
author_role author
author2 García Ordás, María Teresa
García-Olalla Olivera, Óscar
Arenas Díez, Javier
Alaiz Moretón, Héctor
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ingenieria de Sistemas y Automatica
Escuela de Ingenierias Industrial, Informática y Aeroespacial
dc.subject.none.fl_str_mv Energía
Ingeniería de sistemas
Super resolution perception
Super resolution of energy
Data interpolation
Convolutional neural network
Deep-learning
3322.04 Transmisión de Energía
topic Energía
Ingeniería de sistemas
Super resolution perception
Super resolution of energy
Data interpolation
Convolutional neural network
Deep-learning
3322.04 Transmisión de Energía
description [EN] We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which makes it suitable to effectively replace energy apportioning systems. Experiments demonstrate that M-SRPCNN can effectively reconstruct load curves from single month overall values, outperforming traditional apportioning systems.
publishDate 2021
dc.date.none.fl_str_mv 2021
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://www.mdpi.com/1996-1073/14/16/4765
https://hdl.handle.net/10612/21277
url https://www.mdpi.com/1996-1073/14/16/4765
https://hdl.handle.net/10612/21277
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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