Show simple item record

dc.contributor.authorBordin, Chiara
dc.contributor.authorSkjelbred, Hans Ivar
dc.contributor.authorKong, Jiehong
dc.contributor.authorYang, Zhirong
dc.date.accessioned2020-10-19T07:19:10Z
dc.date.available2020-10-19T07:19:10Z
dc.date.created2020-10-03T10:19:31Z
dc.date.issued2020
dc.identifier.citationProcedia Computer Science. 2020, 176 1659-1668.en_US
dc.identifier.issn1877-0509
dc.identifier.urihttps://hdl.handle.net/11250/2683489
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.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleMachine Learning for Hydropower Scheduling: State of the Art and Future Research Directionsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber1659-1668en_US
dc.source.volume176en_US
dc.source.journalProcedia Computer Scienceen_US
dc.identifier.doihttps://doi.org/10.1016/j.procs.2020.09.190
dc.identifier.cristin1836726
dc.relation.projectNorges forskningsråd: 309936en_US
dc.description.localcodeThis article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal