Using Predictive Model of Mean Monthly Flows for Large Open Reservoirs Hydropower Control

Simple item page
dc.contributor.authorMenšík, Pavelcs
dc.contributor.authorStarý, Milošcs
dc.contributor.authorMarton, Danielcs
dc.coverage.issue12cs
dc.coverage.volume89cs
dc.date.issued2014-12-17cs
dc.description.abstractConventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir.en
dc.description.abstractConventional hydropower reservoir operations are mostly based on rules or rule curves. The paper describes algorithm which has been created on idea of adaptive control theory. The adaptive control approach uses repeatedly generated medium-term water flow predictions on a several months ahead as inflows into the large open reservoirs. Values of control outflows are searched by evolution algorithm optimization methods. Principle of the predictive model of average monthly flows is introduced in this paper. The algorithm is applied to the operation hydropower control of selected reservoir.en
dc.formattextcs
dc.format.extent1486-1492cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationProcedia Engineering. 2014, vol. 89, issue 12, p. 1486-1492.en
dc.identifier.doi10.1016/j.proeng.2014.11.435cs
dc.identifier.issn1877-7058cs
dc.identifier.orcid0000-0003-4697-5033cs
dc.identifier.orcid0000-0003-0557-1577cs
dc.identifier.orcid0000-0003-1073-5636cs
dc.identifier.other111274cs
dc.identifier.researcheridU-3553-2017cs
dc.identifier.researcheridAAD-4380-2019cs
dc.identifier.scopus54791204100cs
dc.identifier.scopus33467866300cs
dc.identifier.scopus54791282100cs
dc.identifier.urihttp://hdl.handle.net/11012/69345
dc.language.isoencs
dc.publisherElsevier Science Publisherscs
dc.relation.ispartofProcedia Engineeringcs
dc.relation.urihttp://www.sciencedirect.com/science/article/pii/S1877705814025508cs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unportedcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1877-7058/cs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cs
dc.subjectAdaptiveen
dc.subjectevolution algorithmsen
dc.subjecthydropower functionen
dc.subjectlarge open reservoiren
dc.subjectoperative controlen
dc.subjectoptimizationen
dc.subjectpredictive modelen
dc.subjectAdaptive
dc.subjectevolution algorithms
dc.subjecthydropower function
dc.subjectlarge open reservoir
dc.subjectoperative control
dc.subjectoptimization
dc.subjectpredictive model
dc.titleUsing Predictive Model of Mean Monthly Flows for Large Open Reservoirs Hydropower Controlen
dc.title.alternativeUsing Predictive Model of Mean Monthly Flows for Large Open Reservoirs Hydropower Controlen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-111274en
sync.item.dbtypeVAVen
sync.item.insts2025.10.14 14:46:27en
sync.item.modts2025.10.14 09:34:26en
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav vodního hospodářství krajinycs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1s2.0S1877705814025508main.pdf
Size:
750.2 KB
Format:
Adobe Portable Document Format
Description:
1s2.0S1877705814025508main.pdf
Download
Collections
Ústav vodního hospodářství krajiny