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dc.contributor.authorMert, İlker
dc.contributor.authorÜneş, Fatih
dc.contributor.authorKarakuş, Cuma
dc.contributor.authorJoksimovic, Darko
dc.date.accessioned12.07.201910:50:10
dc.date.accessioned2019-07-12T22:02:48Z
dc.date.available12.07.201910:50:10
dc.date.available2019-07-12T22:02:48Z
dc.date.issued2019
dc.identifier.citationMert, İ., Üneş, F., Karakuş, C., Joksimovic, D. (2019). Estimation of wind energy power using different artificial intelligence techniques and empirical equations. Energy Sources, Part A: Recovery, Utilization and Environmental Effects. https://doi.org/10.1080/15567036.2019.1632981
dc.identifier.urihttps://doi.org/10.1080/15567036.2019.1632981
dc.identifier.urihttps://hdl.handle.net/20.500.12508/462
dc.description.abstractThe understanding of the behavior of a wind turbine is difficult due to changes in weather conditions. To obtain the response of a wind turbine influenced by changes in both wind speed and its direction, using the meteorological station data is often preferred to using the real turbine data. Furthermore, simulated data can be easily extrapolated to varied turbine hub heights. In order to estimate the most effective power output in this study, a wind turbine simulation was developed. The simulation depends on the real meteorological data. For the purpose, three modeling techniques, namely Multi-Nonlinear Regression (MNLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and support vector machines (SVM) were used. In SVM learning process, polynomial and radial basis kernel functions were used. Models were compared to wind turbine measurement values in the same region for similar data. MNLR was used to determine quantify the strength of the relationship between parameters and to eliminate the ineffective parameters. Efficient parameters preferred for training and testing phases of the SVM and ANFIS. The Subtractive Clustering and Grid Partitioning methods were used to identify the inference parameters of ANFIS. According to performance evaluations, MNLR-ANFIS modeling based on Subtractive Clustering gave better results than Grid Partitioning. The results showed that proposed collaborative model could be applied to wind power estimation problems. © 2019, © 2019 Taylor & Francis Group, LLC.en_US
dc.language.isoengen_US
dc.publisherTaylor and Francis Inc.en_US
dc.relation.isversionof10.1080/15567036.2019.1632981en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive neuro-fuzzy inference systemen_US
dc.subjectStepwise regressionen_US
dc.subjectSupport vector machinesen_US
dc.subjectWind energyen_US
dc.subjectWind turbineen_US
dc.subject.classificationWind Speed | Forecasting Performance | Prediction Interval
dc.subject.otherFuzzy neural networks
dc.subject.otherFuzzy systems
dc.subject.otherMeteorology
dc.subject.otherSupport vector machines
dc.subject.otherWind
dc.subject.otherWind power
dc.subject.otherWind turbines
dc.subject.otherAdaptive neuro-fuzzy inference system
dc.subject.otherArtificial intelligence techniques
dc.subject.otherMeteorological station
dc.subject.otherNon-linear regression
dc.subject.otherStepwise regression
dc.subject.otherSubtractive clustering
dc.subject.otherWind turbine measurement
dc.subject.otherWind turbine simulation
dc.subject.otherFuzzy inference
dc.titleEstimation of wind energy power using different artificial intelligence techniques and empirical equationsen_US
dc.typearticleen_US
dc.relation.journalEnergy Sources, Part A: Recovery, Utilization and Environmental Effectsen_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- İnşaat Mühendisliği Bölümüen_US
dc.contributor.departmentMühendislik ve Doğa Bilimleri Fakültesi -- Makina Mühendisliği Bölümü
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.contributor.isteauthorÜneş, Fatih
dc.contributor.isteauthorKarakuş, Cuma
dc.relation.indexScopusen_US


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