dc.creator | Luque Rodríguez, Joaquín | es |
dc.creator | Anguita, Davide | es |
dc.creator | Pérez García, Francisco | es |
dc.creator | Denda, Robert | es |
dc.date.accessioned | 2020-06-13T08:12:24Z | |
dc.date.available | 2020-06-13T08:12:24Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Luque Rodríguez, J., Anguita, D., Pérez García, F. y Denda, R. (2020). Spectral analysis of electricity demand using Hilbert-Huang Transform. Sensors, 20 (10) | |
dc.identifier.issn | 1424-8220 | es |
dc.identifier.uri | https://hdl.handle.net/11441/97766 | |
dc.description.abstract | 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) andWavelet
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 di erent 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. | es |
dc.description.sponsorship | Ministerio de Ciencia, Innovación y Universidades RTI2018-094917-B-I00 | es |
dc.format | application/pdf | es |
dc.format.extent | 25 | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Sensors, 20 (10) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Hilbert–Huang Transform | es |
dc.subject | Empirical Mode Decomposition | es |
dc.subject | Spectral analysis | es |
dc.subject | Electricity demand | es |
dc.title | Spectral analysis of electricity demand using Hilbert-Huang Transform | 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 Tecnología Electrónica | es |
dc.relation.projectID | RTI2018-094917-B-I00 | es |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/20/10/2912 | es |
dc.identifier.doi | 10.3390/s20102912 | es |
dc.journaltitle | Sensors | es |
dc.publication.volumen | 20 | es |
dc.publication.issue | 10 | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |