Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings

[EN] Great concern regarding energy efficiency has led the research community to develop approaches which enhance the energy awareness by means of insightful representations. An example of intuitive energy representation is the parts-based representation provided by Non-Intrusive Load Monitoring (NI...

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Autores: García Pérez, Diego, Pérez López, Daniel, Diaz Blanco, Ignacio, González Muñiz, Ana, Domínguez González, Manuel, Cuadrado Vega, Abel Alberto
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2021
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/18166
Acceso en línea:https://ieeexplore.ieee.org/document/9309369
https://hdl.handle.net/10612/18166
Access Level:acceso abierto
Palabra clave:Ingeniería industrial
Energy efficiency
Building energy consumption
Energy disaggregation
NILM
Denoising auto-encoders
5312.05 Energía
3322
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oai_identifier_str oai:buleria.unileon.es:10612/18166
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repository_id_str
spelling Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential BuildingsGarcía Pérez, DiegoPérez López, DanielDiaz Blanco, IgnacioGonzález Muñiz, AnaDomínguez González, ManuelCuadrado Vega, Abel AlbertoIngeniería industrialEnergy efficiencyBuilding energy consumptionEnergy disaggregationNILMDenoising auto-encoders5312.05 Energía3322[EN] Great concern regarding energy efficiency has led the research community to develop approaches which enhance the energy awareness by means of insightful representations. An example of intuitive energy representation is the parts-based representation provided by Non-Intrusive Load Monitoring (NILM) techniques which decompose non-measured individual loads from a single total measurement of the installation, resulting in more detailed information about how the energy is spent along the electrical system. Although there are previous works that have achieved important results on NILM, the majority of the NILM systems were only validated in residential buildings, leaving a niche for the study of energy disaggregation in non-residential buildings, which present a specific behavior. In this paper, we suggest a novel fully-convolutional denoising auto-encoder architecture (FCN-dAE) as a convenient NILM system for large non-residential buildings, and it is compared, in terms of particular aspects of large buildings, to previous denoising auto-encoder approaches (dAE) using real electrical consumption from a hospital facility. Furthermore, by means of three use cases, we show that our approach provides extra helpful funcionalities for energy management tasks in large buildings, such as meter replacement, gap filling or novelty detection.SIThis work was supported by the Principado de Asturias Government through the Predoctoral Grant “Severo Ochoa.” Paper no. TSG-00737-2020IEEEIngenieria de Sistemas y AutomaticaEscuela de Ingenierias Industrial, Informática y Aeroespacial2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttps://ieeexplore.ieee.org/document/9309369https://hdl.handle.net/10612/18166reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad Rey Juan CarlosInglésinfo:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/181662026-06-24T12:43:27Z
dc.title.none.fl_str_mv Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
title Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
spellingShingle Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
García Pérez, Diego
Ingeniería industrial
Energy efficiency
Building energy consumption
Energy disaggregation
NILM
Denoising auto-encoders
5312.05 Energía
3322
title_short Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
title_full Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
title_fullStr Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
title_full_unstemmed Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
title_sort Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
dc.creator.none.fl_str_mv García Pérez, Diego
Pérez López, Daniel
Diaz Blanco, Ignacio
González Muñiz, Ana
Domínguez González, Manuel
Cuadrado Vega, Abel Alberto
author García Pérez, Diego
author_facet García Pérez, Diego
Pérez López, Daniel
Diaz Blanco, Ignacio
González Muñiz, Ana
Domínguez González, Manuel
Cuadrado Vega, Abel Alberto
author_role author
author2 Pérez López, Daniel
Diaz Blanco, Ignacio
González Muñiz, Ana
Domínguez González, Manuel
Cuadrado Vega, Abel Alberto
author2_role author
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 Ingeniería industrial
Energy efficiency
Building energy consumption
Energy disaggregation
NILM
Denoising auto-encoders
5312.05 Energía
3322
topic Ingeniería industrial
Energy efficiency
Building energy consumption
Energy disaggregation
NILM
Denoising auto-encoders
5312.05 Energía
3322
description [EN] Great concern regarding energy efficiency has led the research community to develop approaches which enhance the energy awareness by means of insightful representations. An example of intuitive energy representation is the parts-based representation provided by Non-Intrusive Load Monitoring (NILM) techniques which decompose non-measured individual loads from a single total measurement of the installation, resulting in more detailed information about how the energy is spent along the electrical system. Although there are previous works that have achieved important results on NILM, the majority of the NILM systems were only validated in residential buildings, leaving a niche for the study of energy disaggregation in non-residential buildings, which present a specific behavior. In this paper, we suggest a novel fully-convolutional denoising auto-encoder architecture (FCN-dAE) as a convenient NILM system for large non-residential buildings, and it is compared, in terms of particular aspects of large buildings, to previous denoising auto-encoder approaches (dAE) using real electrical consumption from a hospital facility. Furthermore, by means of three use cases, we show that our approach provides extra helpful funcionalities for energy management tasks in large buildings, such as meter replacement, gap filling or novelty detection.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://ieeexplore.ieee.org/document/9309369
https://hdl.handle.net/10612/18166
url https://ieeexplore.ieee.org/document/9309369
https://hdl.handle.net/10612/18166
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
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 15.300724

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