Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/28317
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dc.contributor.authorUzlu, Ergun-
dc.contributor.authorÖztürk, Hasan-
dc.contributor.authorNacar, Sinan-
dc.contributor.authorKankal, Murat-
dc.date.accessioned2022-08-23T07:08:08Z-
dc.date.available2022-08-23T07:08:08Z-
dc.date.issued2014-05-01-
dc.identifier.citationUzlu, E. vd .(2014). "Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey". Energy, 69, Special Issue, 638-647.en_US
dc.identifier.issn0360-5442-
dc.identifier.issn1873-6785-
dc.identifier.urihttps://doi.org/10.1016/j.energy.2014.03.059-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0360544214003235-
dc.identifier.urihttp://hdl.handle.net/11452/28317-
dc.description.abstractThe primary objective of this study was to apply the ANN (artificial neural network) model with the ABC (artificial bee colony) algorithm to estimate annual hydraulic energy production of Turkey. GEED (gross electricity energy demand), population, AYT (average yearly temperature), and energy consumption were selected as independent variables in the model. The first part of the study compared ANN-ABC model performance with results of classical ANN models trained with the BP (back propagation) algorithm. Mean square and relative error were applied to evaluate model accuracy. The test set errors emphasized positive differences between the ANN-ABC and classical ANN models. After determining optimal configurations, three different scenarios were developed to predict future hydropower generation values for Turkey. Results showed the ANN-ABC method predicted hydroelectric generation better than the classical ANN trained with the BP algorithm. Furthermore, results indicated future hydroelectric generation in Turkey will range from 69.1 to 76.5 TWh in 2021, and the total annual electricity demand represented by hydropower supply rates will range from 14.8% to 18.0%. However, according to Vision 2023 agenda goals, the country plans to produce 30% of its electricity demand from renewable energy sources by 2023, and use 20% less energy than in 2010. This percentage renewable energy provision cannot be accomplished unless changes in energy policy and investments are not addressed and implemented. In order to achieve this goal, the Turkish government must reconsider and raise its own investments in hydropower, wind, solar, and geothermal energy, particularly hydropower.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevieren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial bee colony algorithmen_US
dc.subjectHydropower generationen_US
dc.subjectNeural networksen_US
dc.subjectTurkeyen_US
dc.subjectElectricity energy-consumptionen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectRenewable energyen_US
dc.subjectWheat productionen_US
dc.subjectDemanden_US
dc.subjectHydropoweren_US
dc.subjectPredictionen_US
dc.subjectProvinceen_US
dc.subjectProjectionsen_US
dc.subjectWateren_US
dc.subjectTurkeyen_US
dc.subjectApoideaen_US
dc.subjectElectricityen_US
dc.subjectEnergy utilizationtr_TR
dc.subjectEvolutionary algorithmsen_US
dc.subjectGeothermal energyen_US
dc.subjectInvestmentsen_US
dc.subjectNeural networksen_US
dc.subjectOptimizationen_US
dc.subjectRenewable energy resourcesen_US
dc.subjectAlgorithmsen_US
dc.subjectBackpropagationen_US
dc.subjectBackpropagation algorithmsen_US
dc.subjectEnergy utilizationen_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectGeothermal energyen_US
dc.subjectHydroelectric poweren_US
dc.subjectNeural networksen_US
dc.subjectANN (artificial neural network)en_US
dc.subjectArtificial bee coloniesen_US
dc.subjectArtificial bee colony algorithmsen_US
dc.subjectBP (back propagation) algorithmen_US
dc.subjectHydro-power generationen_US
dc.subjectHydroelectric generationen_US
dc.subjectAlgorithmen_US
dc.subjectArtificial neural networken_US
dc.subjectBack propagationen_US
dc.subjectDemand analysisen_US
dc.subjectEnergy conservationen_US
dc.subjectEnergy policyen_US
dc.subjectEstimation methoden_US
dc.subjectInvestmenten_US
dc.subjectNumerical modelen_US
dc.subjectState roleen_US
dc.subjectAccuracy assessmenten_US
dc.subjectPower generationen_US
dc.subjectInvestmentsen_US
dc.titleEstimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkeyen_US
dc.typeArticleen_US
dc.identifier.wos000337856100060tr_TR
dc.identifier.scopus2-s2.0-84901497530tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.contributor.departmentUludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.tr_TR
dc.contributor.orcid0000-0002-9042-6851tr_TR
dc.identifier.startpage638tr_TR
dc.identifier.endpage647tr_TR
dc.identifier.volume69tr_TR
dc.identifier.issueSpecial Issueen_US
dc.relation.journalEnergyen_US
dc.contributor.buuauthorAkpınar, Adem-
dc.contributor.researcheridAAC-6763-2019tr_TR
dc.relation.collaborationYurt içitr_TR
dc.subject.wosThermodynamicsen_US
dc.subject.wosEnergy & fuelsen_US
dc.indexed.wosSCIEen_US
dc.indexed.scopusScopusen_US
dc.wos.quartileQ1en_US
dc.contributor.scopusid23026855400tr_TR
dc.subject.scopusArtificial Neural Network; Electricity Demand; Autoregressive Integrated Moving Averageen_US
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