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dc.contributor.authorFedorova, S. V.en
dc.contributor.authorKhudyakov, P. Y.en
dc.contributor.authorMelkozerov, N. A.en
dc.contributor.authorFirsova, D. A.en
dc.date.accessioned2019-07-22T06:48:34Z-
dc.date.available2019-07-22T06:48:34Z-
dc.date.issued2014-
dc.identifier.citationFrom a forecasting methodology for the electric energy consumption of mono-towns to its sustainability / S. V. Fedorova, P. Y. Khudyakov, N. A. Melkozerov et al. // WIT Transactions on Ecology and the Environment. — 2014. — Vol. 186. — P. 185-196.en
dc.identifier.issn1743-3541-
dc.identifier.otherhttp://www.witpress.com/Secure/elibrary/papers/ESUS14/ESUS14016FU1.pdfpdf
dc.identifier.other1good_DOI
dc.identifier.other224cc1f5-5761-4225-93ff-b684a5e52506pure_uuid
dc.identifier.otherhttp://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=84926486278m
dc.identifier.urihttp://elar.urfu.ru/handle/10995/75786-
dc.description.abstractThe need of Russia for a resource-innovative energy strategy for the period up to 2035 makes it urgent to create a methodology of strategic forecasting for the electric energy consumption of mono-towns, which will result in the efficient development of a power supply system, ensuring territories’ sustainability. For this purpose, it is necessary to consider a mono-town as a complete system, a living organism with a definite life cycle and structure of energy consumers. The authors offer a technocoenosis approach to carry out the ranking and structural analysis of the electric energy consumption of one of the Sverdlovsk region’s mono-towns, taking into account dynamics in population, enterprises, organizations and institutions for the period of 5 years. The provided analysis makes it possible to judge the technocoenosis optimality using characteristic exponent β, depending on the structural features of the territory. The authors developed an algorithm for electric energy consumption forecasting, based on Support Vector Machines (SVM), which takes into account the electric energy of mono-towns’ consumers and the climatic factors. Forecasting accuracy was achieved using cross-validation of the input data in order to optimize the training model. The corresponding changes in electric energy consumption, when a characteristic exponent is optimal, will result in a target forecast that provides the sustainable development of mono-towns. © 2014 WIT Press.en
dc.description.sponsorshipInternational Journal of Sustainable Development and Planning;WIT Transactions on Ecology and the Environmenten
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherWITPressen
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceWIT Transactions on Ecology and the Environmenten
dc.subjectELECTRIC ENERGY CONSUMPTIONen
dc.subjectFORECASTING ALGORITHMen
dc.subjectMONO-TOWN SUSTAINABILITYen
dc.subjectSTRATEGYen
dc.subjectTECHNOCOENOSIS APPROACHen
dc.subjectALGORITHMen
dc.subjectARTIFICIAL INTELLIGENCEen
dc.subjectELECTRICITY SUPPLYen
dc.subjectENERGY USEen
dc.subjectFORECASTING METHODen
dc.subjectSUSTAINABILITYen
dc.subjectRUSSIAN FEDERATIONen
dc.subjectSVERDLOVSKen
dc.titleFrom a forecasting methodology for the electric energy consumption of mono-towns to its sustainabilityen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dc.conference.name5th International Conference on Energy and Sustainability, 2014en
dc.conference.date16 December 2014 through 18 December 2014-
dc.identifier.doi10.2495/ESUS140161-
dc.identifier.scopus84926486278-
local.affiliationTechnical University of UGMK, Russian Federationen
local.affiliationUral Federal University, Russian Federationen
local.contributor.employeeФедорова Светлана Владимировнаru
local.contributor.employeeХудяков Павел Юрьевичru
local.contributor.employeeМелкозеров Никита Алексеевичru
local.description.firstpage185-
local.description.lastpage196-
local.volume186-
local.identifier.pure310938-
local.identifier.eid2-s2.0-84926486278-
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