Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/111084
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dc.contributor.authorKhalyasmaa, A.en
dc.contributor.authorMatrenin, P.en
dc.contributor.authorEroshenko, S.en
dc.date.accessioned2022-05-12T08:12:37Z-
dc.date.available2022-05-12T08:12:37Z-
dc.date.issued2022-
dc.identifier.citationKhalyasmaa A. Inappropriate Machine Learning Application in Real Power Industry Cases / A. Khalyasmaa, P. Matrenin, S. Eroshenko. — DOI 10.19181/socjour.2021.27.3.8427 // International Journal of Electrical and Computer Engineering. — 2022. — Vol. 12. — Iss. 3. — P. 3023-3032.en
dc.identifier.issn2088-8708-
dc.identifier.otherAll Open Access, Gold3
dc.identifier.urihttp://hdl.handle.net/10995/111084-
dc.description.abstractGlobal digital transformation of the energy sector has led to the emergence of multiple digital platform solutions, the implementation of which have revealed new problems associated with continuous growth of data volumes requiring new approaches to their processing and analysis. This article is devoted to the improper application of machine learning approaches and flawed interpretation of their output at various stages of decision support systems development: data collection; model development, training and testing as well as industrial implementation. As a real industrial case study, the article examines the power generation forecasting problem of photovoltaic power plants. The authors supplement the revealed problems with the corresponding recommendation for industrial specialists and software developers. © 2022 Institute of Advanced Engineering and Science. All rights reserved.en
dc.description.sponsorshipThe reported study was supported by Russian Foundation for Basic Research RFBR, research project No. 20-010-00911.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherInstitute of Advanced Engineering and Scienceen1
dc.publisherInstitute of Advanced Engineering and Scienceen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceInt. J. Electr. Comput. Eng.2
dc.sourceInternational Journal of Electrical and Computer Engineeringen
dc.subjectDIGITAL TRANSFORMATIONen
dc.subjectINTELLIGENT SYSTEMen
dc.subjectMACHINE LEARNING APPLICATIONen
dc.subjectPHOTOVOLTAIC POWER PLANTSen
dc.subjectPOWER GENERATION FORECASTINGen
dc.titleInappropriate Machine Learning Application in Real Power Industry Casesen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.19181/socjour.2021.27.3.8427-
dc.identifier.scopus85126446498-
local.contributor.employeeKhalyasmaa, A., Electrical Engineering Department, Ural Federal University, Ekaterinburg, Russian Federation, Power Plants Department, Novosibirsk State Technical University, Novosibirsk, Russian Federation; Matrenin, P., Industrial Power Supply Systems Department, Novosibirsk State Technical University, Novosibirsk, Russian Federation; Eroshenko, S., Electrical Engineering Department, Ural Federal University, Ekaterinburg, Russian Federation, Power Plants Department, Novosibirsk State Technical University, Novosibirsk, Russian Federationen
local.description.firstpage3023-
local.description.lastpage3032-
local.issue3-
local.volume12-
local.contributor.departmentElectrical Engineering Department, Ural Federal University, Ekaterinburg, Russian Federation; Power Plants Department, Novosibirsk State Technical University, Novosibirsk, Russian Federation; Industrial Power Supply Systems Department, Novosibirsk State Technical University, Novosibirsk, Russian Federationen
local.identifier.eid2-s2.0-85126446498-
local.fund.rffi20-010-00911
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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