Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network

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  • SDG 7 · Erişilebilir ve Temiz Enerji

Özet

Electricity is one of the most important end-user energy types in today’s world and has an effective role in development of societies and economies. Stability of electricity supply is provided by matching of generated and consumed electricity amount during the all-day. So, electricity consumption forecasting is an essential issue for electric utilities. In this study, the monthly electricity demand of Turkey has been predicted. To model the effects of seasonality and trend, four different ANN models have been developed and selected the superior one. In addition, the selected ANN model has been compared with SARIMA model in order to increase the acceptability and reliability of the ANN model. The monthly electricity demand of Turkey has been predicted between 2015 and 2018 via the ANN model that can make successful and high-accuracy predictions according to the performance measures. The forecasting values will help in determining the medium-term and stable energy policies.

Anahtar Kelimeler

RDL Kalıcı Bağlantı (PID)
https://rdlopen.rawdatalibrary.net/r/gumushane/15047Resmi atıflarda kullanılan kalıcı adres
DOI
https://doi.org/10.1007/s00521-017-3183-5Yayıncı resmi DOI adresi
Tam Kayıt
AlanDeğerDil
dc.contributor.authorHamzaçebi, Coşkun
dc.contributor.authorEs, Hüseyin Avni
dc.contributor.authorÇakmak, Recep
dc.date.accessioned2026-10-22T10:23:30.878303629Z
dc.date.available2026-10-22T10:23:30.878303629Z
dc.date.issued2019
dc.description.abstractElectricity is one of the most important end-user energy types in today’s world and has an effective role in development of societies and economies. Stability of electricity supply is provided by matching of generated and consumed electricity amount during the all-day. So, electricity consumption forecasting is an essential issue for electric utilities. In this study, the monthly electricity demand of Turkey has been predicted. To model the effects of seasonality and trend, four different ANN models have been developed and selected the superior one. In addition, the selected ANN model has been compared with SARIMA model in order to increase the acceptability and reliability of the ANN model. The monthly electricity demand of Turkey has been predicted between 2015 and 2018 via the ANN model that can make successful and high-accuracy predictions according to the performance measures. The forecasting values will help in determining the medium-term and stable energy policies.
dc.identifier.doi10.1007/s00521-017-3183-5
dc.identifier.eissn1433-3058
dc.identifier.endpage2231
dc.identifier.issn0941-0643
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85028358454
dc.identifier.startpage2217
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/gumushane/15047
dc.identifier.volume31
dc.identifier.wosWOS:000478687000020
dc.language.isoen
dc.publisherSpringer London
dc.relation.ispartofNeural Computing and Applications
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial neural network
dc.subjectSARIMA, forecasting
dc.subjectSeasonal electricity demand
dc.subjectTurkey
dc.subject.classificationComputer Science, Artificial Intelligence
dc.subject.classificationComputer Science
dc.subject.otherElectric power utilization
dc.subject.otherElectric utilities
dc.subject.otherNeural networks
dc.subject.otherANN modeling
dc.subject.otherElectricity consumption forecasting
dc.subject.otherElectricity demands
dc.subject.otherElectricity supply
dc.subject.otherHigh-accuracy
dc.subject.otherPerformance measure
dc.subject.otherSarima models
dc.subject.otherTurkey
dc.subject.otherForecasting
dc.titleForecasting of Turkey’s monthly electricity demand by seasonal artificial neural network
dc.typeArticle
dspace.entity.typePublication
rdl.citation.openalex77
rdl.citation.scopus66
rdl.citation.wos51
rdl.oa.statusclosed
rdl.sdgSDG07
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)
rdl.wos.indexSocial Science Citation Index (SSCI)