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dc.contributor.authorEroshenko, S.en
dc.contributor.authorKhalyasmaa, A.en
dc.contributor.authorValiev, R.en
dc.date.accessioned2021-08-31T14:58:59Z-
dc.date.available2021-08-31T14:58:59Z-
dc.date.issued2018-
dc.identifier.citationEroshenko S. Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology / S. Eroshenko, A. Khalyasmaa, R. Valiev. — DOI 10.1051/e3scconf/20185102003 // E3S Web of Conferences. — 2018. — Vol. 51. — P. -.en
dc.identifier.issn25550403-
dc.identifier.otherFinal2
dc.identifier.otherAll Open Access, Gold, Green3
dc.identifier.otherhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85073871625&doi=10.1051%2fe3scconf%2f20185102003&partnerID=40&md5=104bde459585ecdd2984b82548377893
dc.identifier.otherhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2018/26/e3sconf_icacer2018_02003.pdfm
dc.identifier.urihttp://elar.urfu.ru/handle/10995/101686-
dc.description.abstractIn conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach. © The Authors, published by EDP Sciences.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherEDP Sciencesen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceE3S Web Conf.2
dc.sourceE3S Web of Conferencesen
dc.subjectPOWER PLANTSen
dc.subjectSOLAR ENERGYen
dc.subjectSOLAR POWER GENERATIONen
dc.subjectEXPONENTIAL SMOOTHINGen
dc.subjectFORECAST ERRORSen
dc.subjectMATHEMATICAL APPARATUSen
dc.subjectOPERATING MODESen
dc.subjectOPERATIONAL FORECASTINGen
dc.subjectRENEWABLE ENERGY SOURCEen
dc.subjectSHORT TERM PLANNINGen
dc.subjectSOLAR POWER STATIONen
dc.subjectFORECASTINGen
dc.titleVery-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodologyen
dc.typeConference Paperen
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.1051/e3scconf/20185102003-
dc.identifier.scopus85073871625-
local.contributor.employeeEroshenko, S., Ural Federal University, 620002 Mira str. 19, Ekaterinburg, Russian Federation
local.contributor.employeeKhalyasmaa, A., Ural Federal University, 620002 Mira str. 19, Ekaterinburg, Russian Federation
local.contributor.employeeValiev, R., Ural Federal University, 620002 Mira str. 19, Ekaterinburg, Russian Federation
local.volume51-
dc.identifier.wos000454427500012-
local.contributor.departmentUral Federal University, 620002 Mira str. 19, Ekaterinburg, Russian Federation
local.identifier.pure8555025-
local.identifier.eid2-s2.0-85073871625-
local.identifier.wosWOS:000454427500012-
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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