Ata R.2024-07-222024-07-22200913030914http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/18760This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the power factor of a wind turbine. This model based on the parameters involved for NACA 4415 and LS- 1 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the power factor was taken as output variable. After a successful learning and training process the proposed model produced reasonable mean errors. The results on a testing data indicate that the ANFIS model is found to be more successful than the ANN approach in estimating the power factor.EnglishElectric power factorEnginesFuzzy inferenceFuzzy systemsWell pumpsWind turbinesAdaptive neuro-fuzzy inference systemANFISANFIS modelBlade numbersEnergyInput variablesLearning and trainingMean errorsModel developmentModel-basedOutput variablesPower factorPower factorsSchmitzTesting dataWind powerArticle