A new hybrid deep neural architectural search based ensemble reinforcement learning strategy for wind power forecasting

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dc.contributor.authorJalali, S. M. J.
dc.contributor.authorAhmadian, S.
dc.contributor.authorCampos, Vasco M. A.
dc.contributor.authorShafie-khah, Miadreza
dc.contributor.authorKhosravi, A.
dc.contributor.authorCatalão, João P. S.
dc.contributor.authorOsório, Gerardo J.
dc.date.accessioned2022-02-01T11:34:04Z
dc.date.available2022-02-01T11:34:04Z
dc.date.issued2022-01
dc.description.abstractWind power instability and inconsistency involve the reliability of renewable power energy, the safety of the transmission system, the electrical grid stability and the rapid developments of energy market. The study on wind power forecasting is quite important at this stage in order to facilitate maximum wind energy growth as well as better efficiency of electrical power systems. In this work, we propose a novel hybrid data-driven model based on the concepts of deep learning based convolutional-long short-term memory (CLSTM), mutual information, evolutionary algorithm, neural architectural search procedure, and ensemble-based deep reinforcement learning strategies. We name this hybrid model as DOCREL. In the first step, the mutual information extracts the most effective characteristics from raw wind power time-series datasets. Secondly, we develop an improved version of the evolutionary whale optimization algorithm in order to effectively optimize the architecture of the deep CLSTM models by performing the neural architectural search procedure. At the end, our proposed deep reinforcement learning based ensemble algorithm integrates the optimized deep learning models to achieve the lowest possible wind power forecasting errors for two wind power datasets. In comparison with fourteen state of the art deep learning models, our proposed DOCREL algorithm represents an excellent performance seasonally for two different case studies.pt_PT
dc.identifier.citationJalali, S. M. J., Osório, G. J., Ahmadian, S., Lotfi, M, Campos, V. M. A, Shafie-khah, M., Khosravi, A., & Catalão, J. P. S. (2022). A new hybrid deep neural architectural search based ensemble reinforcement learning strategy for wind power forecasting. IEEE Transactions on Industry Applications, 58(1), 15-27. doi: 10.1109/TIA.2021.3126272. Disponível no Repositório UPT, http://hdl.handle.net/11328/3914pt_PT
dc.identifier.doi10.1109/TIA.2021.3126272pt_PT
dc.identifier.issn0093-9994 (Print)
dc.identifier.issn1939-9367 (Electronic)
dc.identifier.urihttp://hdl.handle.net/11328/3914
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.rightsrestricted accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectWind power forecastingpt_PT
dc.subjectDeep neural architectural searchpt_PT
dc.subjectAadvanced evolutionary algorithmpt_PT
dc.subjectEnsemble reinforcement learning strategypt_PT
dc.subjectHybrid modelpt_PT
dc.titleA new hybrid deep neural architectural search based ensemble reinforcement learning strategy for wind power forecastingpt_PT
dc.typejournal articlept_PT
degois.publication.firstPage15pt_PT
degois.publication.issue1pt_PT
degois.publication.lastPage27pt_PT
degois.publication.titleIEEE Transactions on Industry Applicationspt_PT
degois.publication.volume58pt_PT
dspace.entity.typePublicationen
person.affiliation.nameREMIT – Research on Economics, Management and Information Technologies
person.familyNameOsório
person.givenNameGerardo J.
person.identifier.ciencia-idBD19-D0AD-65DB
person.identifier.gsidt13DoaMAAAAJ
person.identifier.orcid0000-0001-8328-9708
person.identifier.ridC-3616-2014
person.identifier.scopus-author-id54783251300
relation.isAuthorOfPublication7ce5da40-610d-4361-a87f-5cbdfe392256
relation.isAuthorOfPublication.latestForDiscovery7ce5da40-610d-4361-a87f-5cbdfe392256

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