Publication:
Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey

dc.contributor.authorUzlu, Ergun
dc.contributor.authorÖztürk, Hasan
dc.contributor.authorNacar, Sinan
dc.contributor.authorKankal, Murat
dc.contributor.buuauthorAkpınar, Adem
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentİnşaat Mühendisliği Bölümü
dc.contributor.orcid0000-0002-9042-6851
dc.contributor.researcheridAAC-6763-2019
dc.contributor.scopusid23026855400
dc.date.accessioned2022-08-23T07:08:08Z
dc.date.available2022-08-23T07:08:08Z
dc.date.issued2014-05-01
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.
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.
dc.identifier.endpage647
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.issueSpecial Issue
dc.identifier.scopus2-s2.0-84901497530
dc.identifier.startpage638
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.identifier.volume69
dc.identifier.wos000337856100060
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherPergamon-Elsevier
dc.relation.collaborationYurt içi
dc.relation.journalEnergy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial bee colony algorithm
dc.subjectHydropower generation
dc.subjectNeural networks
dc.subjectTurkey
dc.subjectElectricity energy-consumption
dc.subjectParticle swarm optimization
dc.subjectRenewable energy
dc.subjectWheat production
dc.subjectDemand
dc.subjectHydropower
dc.subjectPrediction
dc.subjectProvince
dc.subjectProjections
dc.subjectWater
dc.subjectTurkey
dc.subjectApoidea
dc.subjectElectricity
dc.subjectEnergy utilization
dc.subjectEvolutionary algorithms
dc.subjectGeothermal energy
dc.subjectInvestments
dc.subjectNeural networks
dc.subjectOptimization
dc.subjectRenewable energy resources
dc.subjectAlgorithms
dc.subjectBackpropagation
dc.subjectBackpropagation algorithms
dc.subjectEnergy utilization
dc.subjectEvolutionary algorithms
dc.subjectGeothermal energy
dc.subjectHydroelectric power
dc.subjectNeural networks
dc.subjectANN (artificial neural network)
dc.subjectArtificial bee colonies
dc.subjectArtificial bee colony algorithms
dc.subjectBP (back propagation) algorithm
dc.subjectHydro-power generation
dc.subjectHydroelectric generation
dc.subjectAlgorithm
dc.subjectArtificial neural network
dc.subjectBack propagation
dc.subjectDemand analysis
dc.subjectEnergy conservation
dc.subjectEnergy policy
dc.subjectEstimation method
dc.subjectInvestment
dc.subjectNumerical model
dc.subjectState role
dc.subjectAccuracy assessment
dc.subjectPower generation
dc.subjectInvestments
dc.subject.scopusArtificial Neural Network; Electricity Demand; Autoregressive Integrated Moving Average
dc.subject.wosThermodynamics
dc.subject.wosEnergy & fuels
dc.titleEstimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey
dc.typeArticle
dc.wos.quartileQ1
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/İnşaat Mühendisliği Bölümü
local.indexed.atScopus
local.indexed.atWOS

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