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Short-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithm

2018-09-25Journal article Restricted access
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dc.contributor.authorBento, P.M.R.
dc.contributor.authorPombo, José Álvaro Nunes
dc.contributor.authorMariano, S.
dc.contributor.authorCalado, M. Do Rosário
dc.date.accessioned2020-01-10T17:05:41Z
dc.date.available2020-01-10T17:05:41Z
dc.date.issued2018-09-25
dc.description.abstractShort-term load forecasting plays a preponderant role in the daily basis system's operation and planning. The state- of-the-art comprises a far-reaching set of methodologies, which are traditionally based on time-series analysis and multilayer neural networks. In particular, the existence of countless neural network architectures has highlighted its ability to cope with 'hard' nonlinear approximation tasks, thus making them appropriate to perform load forecasts. Following this successful path, long short-term memory networks were employed in an optimized arrangement as forecasters, this type of recurrent neural networks has received in recent years a renewed interest for machine learning tasks. Firstly, a preprocessing stage takes place, where through the selection of similar days and correlation analysis, meaningful statistics and characteristics are extracted from the load time-series, to assemble the proper training sets. Then, Bat Algorithm is used to excel the long short-term memory network functioning, by fine-tuning its size and its learning hyperparameters. Numerical testing conducted on the Portuguese load time-series reveals promising forecasting results in an overall assessment, when compared with other state-of-the-art methods.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/IS.2018.8710498pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/8221
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherInstitute of Electrical and Electronics Engineerspt_PT
dc.subjectBat algorithmpt_PT
dc.subjectEvolutionary long short term memory networkspt_PT
dc.subjectGradient descent optimizationpt_PT
dc.subjectHyperparameters optimizationpt_PT
dc.subjectShort-term load forecastingpt_PT
dc.subjectSimilar day selectionpt_PT
dc.titleShort-Term Load Forecasting using optimized LSTM Networks via Improved Bat Algorithmpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.conferencePlaceFunchal, Portugalpt_PT
oaire.citation.endPage357pt_PT
oaire.citation.startPage351pt_PT
oaire.citation.title9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, IS 2018pt_PT
person.familyNameRocha Bento
person.familyNamePombo
person.familyNamePinto Simões Mariano
person.familyNameCalado
person.givenNamePedro Miguel
person.givenNameJose
person.givenNameSílvio José
person.givenNameM. do Rosário
person.identifier.ciencia-id7615-8E00-8084
person.identifier.ciencia-id541F-E2B4-D66D
person.identifier.ciencia-id9115-032B-370B
person.identifier.orcid0000-0002-9102-7086
person.identifier.orcid0000-0002-8727-0067
person.identifier.orcid0000-0002-6102-5872
person.identifier.orcid0000-0002-5206-487X
person.identifier.ridN-6834-2013
person.identifier.ridN-6809-2013
person.identifier.scopus-author-id57196424786
person.identifier.scopus-author-id34977533800
person.identifier.scopus-author-id35612517200
person.identifier.scopus-author-id9338016700
rcaap.embargofctCopyright cedido à editora no momento da publicaçãopt_PT
rcaap.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscoverycdbb9afc-4123-45ca-a946-89bafda7ab68

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