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Data-driven state-of-charge prediction of a storage cell using abc/gbrt, abc/mlp and lasso machine Learning techniques

dc.contributor.authorÁlvarez Antón, Juan Carlos 
dc.contributor.authorGarcía Nieto, Paulino José 
dc.contributor.authorGarcía Gonzalo, María Esperanza 
dc.contributor.authorGonzález Vega, Manuela 
dc.contributor.authorBlanco Viejo, Cecilio José 
dc.date.accessioned2023-10-25T08:55:30Z
dc.date.available2023-10-25T08:55:30Z
dc.date.issued2023
dc.identifier.citationJournal of Computational and Applied mathematics, 433, (2023); doi:10.1016/j.cam.2023.115305
dc.identifier.issn0377-0427
dc.identifier.urihttps://hdl.handle.net/10651/70010
dc.description.sponsorshipThe authors wish to acknowledge the computational support provided by the Department of Mathematics at the University of Oviedo. Furthermore, this work was supported by the Spanish State Research Agency (Ministry of Science and Innovation) under Grant MCI-20-PID2019-110955RB-I00
dc.language.isoeng
dc.relation.ispartofJournal of Computational and Applied mathematics
dc.rights© 2023 The Author(s).
dc.rightsCC Reconocimiento – No Comercial – Sin Obra Derivada 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceScopus
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85159354065&doi=10.1016%2fj.cam.2023.115305&partnerID=40&md5=80f3829008c4ab28c31bf5a1af325f1e
dc.titleData-driven state-of-charge prediction of a storage cell using abc/gbrt, abc/mlp and lasso machine Learning techniques
dc.typejournal article
dc.identifier.doi10.1016/j.cam.2023.115305
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-110955RB-I00/ES/NUEVAS TECNOLOGIAS DE BATERIA Y METODOS DE GESTION PARA UN TRANSPORTE PUBLICO URBANO EFICIENTE Y SOSTENIBLE/ 
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.cam.2023.115305
dc.rights.accessRightsopen access
dc.type.hasVersionVoR


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