Evaluation of wind energy investment with artificial neural networks

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dc.authoridMehmet Yavuz: 0000-0002-3966-6518
dc.authoridHasan Hüseyin Yıldırım: 0000-0002-5840-8418
dc.contributor.authorYıldırım, Hasan Hüseyin
dc.contributor.authorYavuz, Mehmet
dc.date.accessioned2020-01-18T21:11:02Z
dc.date.available2020-01-18T21:11:02Z
dc.date.issued2019
dc.departmentNEÜ, Fen Fakültesi, Matematik ve Bilgisayar Bilimleri Bölümüen_US
dc.description.abstractCountries aiming for sustainability in economic growth and development ensurethe reliability of energy supplies. For countries to provide their energy needsuninterruptedly, it is important for domestic and renewable energy sources to beutilised. For this reason, the supply of reliable and sustainable energy has becomean important issue that concerns and occupies mankind. Of the renewable energysources, wind energy is a clean, reliable and inexhaustible source of energy withlow operating costs. Turkey is a rich nation in terms of wind energy potential.Forecasting of investment efficiency is an important issue before and during theinvestment period in wind energy investment process because of high investmentcosts. It is aimed to forecast the wind energy products monthly with multilayerneural network approach in this study. For this aim a feed forward backpropagation neural network model has been established. As a set of data, windspeed values 48 months (January 2012-December 2015) have been used. Thetraining data set occurs from 36 monthly wind speed values (January 2012-December 2014) and the test data set occurs from other values (January-December2015). Analysis findings show that the trained Artificial Neural Networks (ANNs)have the ability of accurate prediction for the samples that are not used at trainingphase. The prediction errors for the wind energy plantation values are rangedbetween 0.00494-0.015035. Also the overall mean prediction error for thisprediction is calculated as 0.004818 (0.48%). In general, we can say that ANNs beable to estimate the aspect of wind energy plant productions.en_US
dc.identifier.citationYavuz, M., Yıldırım, H. H. (2019). Evaluation of wind energy investment with artificial neural networks. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 9, 2, 142-147.en_US
dc.identifier.doi10.11121/ijocta.01.2019.00780en_US
dc.identifier.endpage147en_US
dc.identifier.issn2146-0957en_US
dc.identifier.issn2146-5703en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage142en_US
dc.identifier.urihttps://dx.doi.org/10.11121/ijocta.01.2019.00780
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TXpFeU9EZ3hNUT09/evaluation-of-wind-energy-investment-with-artificial-neural-networks
dc.identifier.urihttps://hdl.handle.net/20.500.12452/2243
dc.identifier.volume9en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofAn International Journal of Optimization and Control: Theories & Applications (IJOCTA)en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US]
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnergy
dc.subjectWind energy
dc.subjectForecasting
dc.subjectEnergy investment evaluation
dc.subjectArtificial neural networks
dc.titleEvaluation of wind energy investment with artificial neural networksen_US
dc.typeArticleen_US

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