Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/139925
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dc.contributor.authorNeshat, M.-
dc.contributor.authorNezhad, M.M.-
dc.contributor.authorAbbasnejad, E.-
dc.contributor.authorMirjalili, S.-
dc.contributor.authorGroppi, D.-
dc.contributor.authorHeydari, A.-
dc.contributor.authorTjernberg, L.B.-
dc.contributor.authorAstiaso Garcia, D.-
dc.contributor.authorAlexander, B.-
dc.contributor.authorShi, Q.-
dc.contributor.authorWagner, M.-
dc.date.issued2021-
dc.identifier.citationEnergy, 2021; 229:120617-1-120617-24-
dc.identifier.issn0360-5442-
dc.identifier.issn1873-6785-
dc.identifier.urihttps://hdl.handle.net/2440/139925-
dc.descriptionAvailable online 18 April 2021-
dc.description.abstractAbstract not available-
dc.description.statementofresponsibilityMehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad, Seyedali Mirjalili, Daniele Groppi, Azim Heydari, Lina Bertling Tjernberg, Davide Astiaso Garcia, Bradley Alexander, Qinfeng Shi, Markus Wagner-
dc.language.isoen-
dc.publisherElsevier-
dc.rights© 2021 Elsevier Ltd. All rights reserved.-
dc.source.urihttp://dx.doi.org/10.1016/j.energy.2021.120617-
dc.subjectNeuro-evolutionary algorithms; Alternating optimisation algorithm; Recurrent deep learning; Long short-term memory neural network; Adaptive variational mode decomposition; Power prediction model; Wind turbin; Power curve-
dc.titleWind turbine power output prediction using a new hybrid neuro-evolutionary method-
dc.typeJournal article-
dc.identifier.doi10.1016/j.energy.2021.120617-
pubs.publication-statusPublished-
dc.identifier.orcidNeshat, M. [0000-0002-9537-9513]-
dc.identifier.orcidAlexander, B. [0000-0003-4118-2798]-
dc.identifier.orcidShi, Q. [0000-0002-9126-2107]-
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]-
Appears in Collections:Australian Institute for Machine Learning publications
Computer Science publications

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