Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform

The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (W...

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Autores: Luque Rodríguez, Joaquín, Anguita, Davide, Pérez García, Francisco, Denda, Robert
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/98200
Acceso en línea:https://hdl.handle.net/11441/98200
https://doi.org/10.3390/s20102912
Access Level:acceso abierto
Palabra clave:Hilbert–Huang Transform
Empirical Mode Decomposition
spectral analysis
electricity demand
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spelling Spectral Analysis of Electricity Demand Using Hilbert–Huang TransformLuque Rodríguez, JoaquínAnguita, DavidePérez García, FranciscoDenda, RobertHilbert–Huang TransformEmpirical Mode Decompositionspectral analysiselectricity demandThe large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.MDPIElectrónica y Electromagnetismo2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/98200https://doi.org/10.3390/s20102912reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésSensors, 20 (10), 1-25.http://dx.doi.org/10.3390/s20102912info:eu-repo/semantics/openAccessoai:idus.us.es:11441/982002026-06-17T12:51:07Z
dc.title.none.fl_str_mv Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
title Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
spellingShingle Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
Luque Rodríguez, Joaquín
Hilbert–Huang Transform
Empirical Mode Decomposition
spectral analysis
electricity demand
title_short Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
title_full Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
title_fullStr Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
title_full_unstemmed Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
title_sort Spectral Analysis of Electricity Demand Using Hilbert–Huang Transform
dc.creator.none.fl_str_mv Luque Rodríguez, Joaquín
Anguita, Davide
Pérez García, Francisco
Denda, Robert
author Luque Rodríguez, Joaquín
author_facet Luque Rodríguez, Joaquín
Anguita, Davide
Pérez García, Francisco
Denda, Robert
author_role author
author2 Anguita, Davide
Pérez García, Francisco
Denda, Robert
author2_role author
author
author
dc.contributor.none.fl_str_mv Electrónica y Electromagnetismo
dc.subject.none.fl_str_mv Hilbert–Huang Transform
Empirical Mode Decomposition
spectral analysis
electricity demand
topic Hilbert–Huang Transform
Empirical Mode Decomposition
spectral analysis
electricity demand
description The large amount of sensors in modern electrical networks poses a serious challenge in the data processing side. For many years, spectral analysis has been one of the most used approaches to extract physically meaningful information from a sea of data. Fourier Transform (FT) and Wavelet Transform (WT) are by far the most employed tools in this analysis. In this paper we explore the alternative use of Hilbert–Huang Transform (HHT) for electricity demand spectral representation. A sequence of hourly consumptions, spanning 40 months of electrical demand in Spain, has been used as dataset. First, by Empirical Mode Decomposition (EMD), the sequence has been time-represented as an ensemble of 13 Intrinsic Mode Functions (IMFs). Later on, by applying Hilbert Transform (HT) to every IMF, an HHT spectrum has been obtained. Results show smoother spectra with more defined shapes and an excellent frequency resolution. EMD also fosters a deeper analysis of abnormal electricity demand at different timescales. Additionally, EMD permits information compression, which becomes very significant for lossless sequence representation. A 35% reduction has been obtained for the electricity demand sequence. On the negative side, HHT demands more computer resources than conventional spectral analysis techniques.
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/11441/98200
https://doi.org/10.3390/s20102912
url https://hdl.handle.net/11441/98200
https://doi.org/10.3390/s20102912
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Sensors, 20 (10), 1-25.
http://dx.doi.org/10.3390/s20102912
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
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score 15,300719

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