Türkiye's energy projection for 2050

dc.contributor.author Selen Cekinir
dc.contributor.author Onder Ozgener
dc.contributor.author Leyla Ozgener
dc.date.accessioned 2025-10-06T17:49:48Z
dc.date.issued 2022
dc.description.abstract In this study Türkiye's energy projection has been evaluated based on four different scenarios for 2050. Firstly Türkiye's electricity production amount in 2050 has been predicted by using an artificial neural network model in MATLAB. According to the prediction it has been revealed that to what extent which scenario can meet this production value. While doing this it has been considered whether Türkiye could meet its energy needs independently by using only its own resources. Then it has been determined how much production increase should be required for each domestic resource by 2050 to meet 100% of the need in each scenario. Also these scenarios have been evaluated and it has been decided which one is more important for energy independence and which one is more important for emission decrease. This is the first study that makes both an energy prediction for 2050 and reveals whether Türkiye could achieve its energy targets and what needs to do to achieve it. This study aims to be a guide for determining Türkiye's energy policies. © 2023 Elsevier B.V. All rights reserved.
dc.identifier.doi 10.1016/j.ref.2022.09.003
dc.identifier.issn 17550084
dc.identifier.issn 1755-0084
dc.identifier.uri https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138616881&doi=10.1016%2Fj.ref.2022.09.003&partnerID=40&md5=402c4e0ea450631c622f6c71d3b6f598
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/8633
dc.language.iso English
dc.publisher Elsevier Ltd
dc.relation.ispartof Renewable Energy Focus
dc.source Renewable Energy Focus
dc.subject Energy, Energy Consumption, Energy Policy, Modelling, Prediction, Energy Policy, Forecasting, Neural Networks, Artificial Neural Network Modeling, Domestic Resources, Electricity Production, Energy, Energy Needs, Energy Projections, Energy-consumption, Modeling, Production Increase, Production Value, Energy Utilization
dc.subject Energy policy, Forecasting, Neural networks, Artificial neural network modeling, Domestic resources, Electricity production, Energy, Energy needs, Energy projections, Energy-consumption, Modeling, Production increase, Production value, Energy utilization
dc.title Türkiye's energy projection for 2050
dc.type Article
dspace.entity.type Publication
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gdc.bip.influenceclass C4
gdc.bip.popularityclass C4
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.endpage 116
gdc.description.startpage 93
gdc.description.volume 43
gdc.identifier.openalex W4297091089
gdc.index.type Scopus
gdc.oaire.diamondjournal false
gdc.oaire.impulse 17.0
gdc.oaire.influence 3.0858904E-9
gdc.oaire.isgreen false
gdc.oaire.keywords PArticle Swarm Optimization
gdc.oaire.keywords Energy
gdc.oaire.keywords Climate-Change
gdc.oaire.keywords Artificial Neural-Networks
gdc.oaire.keywords Sustainable Development
gdc.oaire.keywords Geothermal-Energy
gdc.oaire.keywords Modelling
gdc.oaire.keywords Energy consumption
gdc.oaire.keywords Colony Optimization Approach
gdc.oaire.keywords Demand Estimation
gdc.oaire.keywords Particle Swarm Optimization
gdc.oaire.keywords Wind Energy
gdc.oaire.keywords Renewable Energy
gdc.oaire.keywords Electricity Consumption
gdc.oaire.keywords Prediction
gdc.oaire.keywords Energy policy
gdc.oaire.popularity 1.4314009E-8
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gdc.oaire.sciencefields 0211 other engineering and technologies
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.openalex.collaboration National
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gdc.openalex.normalizedpercentile 0.89
gdc.opencitations.count 14
gdc.plumx.crossrefcites 15
gdc.plumx.mendeley 54
gdc.plumx.scopuscites 12
oaire.citation.endPage 116
oaire.citation.startPage 93
person.identifier.scopus-author-id Cekinir- Selen (57660498900), Ozgener- Onder (6602415625), Ozgener- Leyla (6506529474)
publicationvolume.volumeNumber 43
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