Show simple item record

dc.contributor.authorBordin, Chiara
dc.contributor.authorSkjelbred, Hans Ivar
dc.contributor.authorKong, Jiehong
dc.contributor.authorYang, Zhirong
dc.date.accessioned2020-10-19T10:53:18Z
dc.date.available2020-10-19T10:53:18Z
dc.date.issued2020-10-02
dc.description.abstractThis paper investigates and discusses the current and future role of machine learning (ML) within the hydropower sector. An overview of the main applications of ML in the field of hydropower operations is presented to show the most common topics that have been addressed in the scientific literature in the last years. The objective is to provide recommendations for novel research directions that can be taken in the near future to cover those areas that have not been studied so far. The key contribution of this paper lies in a critical investigation of the state of the art of ML applications in hydropower scheduling. In light of the established literature available in the last years, this study identifies and discusses new roles that can be covered by ML, coupled with cyber-physical systems (CPSs), with a particular focus on short-term hydropower scheduling (STHS) challenges.en_US
dc.identifier.citationBordin C, Skjelbred HI, Kong J, Yang Z. Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions. Procedia Computer Science. 2020;176:1659-1668en_US
dc.identifier.cristinIDFRIDAID 1836726
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.09.190
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/10037/19622
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalProcedia Computer Science
dc.relation.projectIDNorges forskningsråd: 309936en_US
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/ENERGIX/309936/Norway/The intelligent decision-making process for hydro scheduling//en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420en_US
dc.titleMachine Learning for Hydropower Scheduling: State of the Art and Future Research Directionsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


File(s) in this item

Thumbnail

This item appears in the following collection(s)

Show simple item record