Akademik Veri Yönetim Sistemi
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, cilt.34, sa.4, ss.2203-2213, 2019 (SCI-Expanded)
In this study, wind energy potential of Sinop and Adiyaman provinces in different regions of Turkey were analyzed statistically, based on the hourly measured data by Directorate of State Meteorological Station in 2008-2017 years. During the statistical analysis, average wind speed, standard deviation of wind speed, maximum wind speed and wind power density were determined. The Weibull distribution function was used to the distribution of wind speed and determination of wind power intensity. For the power density values obtained as a result of the study, a predictive model was established with the support vector machine (SVM) regression. Polynomial kernel, normalized polynomial kernel, radial basis function (RBF) kernel and Pearson universal kernel VII (PUK) models were used in SVM regression. Mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative square error (RRSE) error analyzes were performed for SVM regression estimates. The best estimation of wind power density predictive models generated by 4 different kernel functions using SVM regression was shown to belong to the polynomial kernel.