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Optimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecasting

dc.contributor.authorBento, P.M.R.
dc.contributor.authorPombo, José Álvaro Nunes
dc.contributor.authorCalado, M. Do Rosário
dc.contributor.authorMariano, S.
dc.date.accessioned2019-07-23T09:32:38Z
dc.date.available2019-07-23T09:32:38Z
dc.date.issued2019-09-17
dc.description.abstractShort-term load forecasting is very important for reliable power system operation, even more so under electricity market deregulation and integration of renewable resources framework. This paper presents a new enhanced method for one day ahead load forecast, combing improved data selection and features extraction techniques (similar/recent day-based selection, correlation and wavelet analysis), which brings more “regularity” to the load time-series, an important precondition for the successful application of neural networks. A combination of Bat and Scaled Conjugate Gradient Algorithms is proposed to improve neural network learning capability. Another feature is the method's capacity to fine-tune neural network architecture and wavelet decomposition, for which there is no optimal paradigm. Numerical testing using the Portuguese national system load, and the regional (state) loads of New England and New York, revealed promising forecasting results in comparison with other state-of-the-art methods, therefore proving the effectiveness of the assembled methodology.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.neucom.2019.05.030pt_PT
dc.identifier.issn09252312
dc.identifier.urihttp://hdl.handle.net/10400.6/7142
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.subjectArtificial neural networkspt_PT
dc.subjectBat algorithmpt_PT
dc.subjectFeatures extractionpt_PT
dc.subjectImproved data selectionpt_PT
dc.subjectShort-term load forecastpt_PT
dc.subjectWavelet transformpt_PT
dc.titleOptimization of neural network with wavelet transform and improved data selection using bat algorithm for short-term load forecastingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage71pt_PT
oaire.citation.startPage53pt_PT
oaire.citation.titleNeurocomputingpt_PT
oaire.citation.volume358pt_PT
person.familyNameRocha Bento
person.familyNamePombo
person.familyNameCalado
person.familyNamePinto Simões Mariano
person.givenNamePedro Miguel
person.givenNameJose
person.givenNameM. do Rosário
person.givenNameSílvio José
person.identifier.ciencia-id7615-8E00-8084
person.identifier.ciencia-id9115-032B-370B
person.identifier.ciencia-id541F-E2B4-D66D
person.identifier.orcid0000-0002-9102-7086
person.identifier.orcid0000-0002-8727-0067
person.identifier.orcid0000-0002-5206-487X
person.identifier.orcid0000-0002-6102-5872
person.identifier.ridN-6809-2013
person.identifier.ridN-6834-2013
person.identifier.scopus-author-id57196424786
person.identifier.scopus-author-id34977533800
person.identifier.scopus-author-id9338016700
person.identifier.scopus-author-id35612517200
rcaap.embargofctCopyright cedido à editora no momento da publicação.pt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublicationcdbb9afc-4123-45ca-a946-89bafda7ab68
relation.isAuthorOfPublication.latestForDiscoverycce2060a-24b8-441b-8896-cb4d0b3d3e83

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