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Feature extraction for nonintrusive load monitoring based on S-Transform

dc.contributor.authorJiménez, Yuliethspa
dc.contributor.authorDuarte, Cesar A.spa
dc.contributor.authorPetit, Johannspa
dc.contributor.authorCarrillo Caicedo, Gilbertospa
dc.date.accessioned2019-08-08T16:18:24Zspa
dc.date.available2019-08-08T16:18:24Zspa
dc.date.issued2014-05-01spa
dc.description.abstractThe electric energy demand is dramatically growing worldwide and demand reduction emerges as an outstanding strategy; it implies detailed information about the electricity consumption, namely load disaggregation. Typical automatic methods for load disaggregation require high hardware efforts to install one sensor per appliance, whereas Non-intrusive Load Monitoring (NILM) systems diminish the hardware efforts through signal processing and mathematical modeling. One approach to NILM systems is to model the load signatures via artificial intelligence. This paper proposes to employ S-Transform for the feature extraction stage and Support Vector Machines for the pattern recognition problem. Several experiments are presented and the results of the feature extraction with S-Transform and Wavelet Packet Transform are compared. Thus promising feature vectors based on S-Transform are presented with similar or superior performance than the approach based on Wavelet Packet Transform.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doi10.1109/PSC.2014.6808109spa
dc.identifier.isbn9781479939602spa
dc.identifier.urihttps://repositorio.udes.edu.co/handle/001/3549spa
dc.language.isoengspa
dc.relation.ispartofClemson University Power Systems Conference, 2014eng
dc.rightsDerechos Reservados - Universidad de Santander, 2014spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/spa
dc.sourcehttps://ieeexplore.ieee.org/document/6808109eng
dc.subject.proposalFeature extractioneng
dc.subject.proposalNonintrusive load monitoringeng
dc.subject.proposalStockwell transformeng
dc.subject.proposalSupport vector machineeng
dc.subject.proposalWavelet transformeng
dc.titleFeature extraction for nonintrusive load monitoring based on S-Transformeng
dc.typeDocumento de Conferenciaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_c94fspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/conferenceObjectspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dspace.entity.typePublication
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
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