Daş, Mehmet; Balpetek, Nilay; Akpınar, Ebru Kavak; Akpınar, Sinan

In this study, wind energy potential of Sinop and Adıyaman 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.

Das, Mehmet; Akpinar, Ebru Kavak

In this study, an air heated solar collector (AHSC) dryer was designed to determine the drying characteristics of the pear. Flat pear slices of 10 mm thickness were used in the experiments. The pears were dried both in the AHSC dryer and under the sun. Panel glass temperature, panel floor temperature, panel inlet temperature, panel outlet temperature, drying cabinet inlet temperature, drying cabinet outlet temperature, drying cabinet temperature, drying cabinet moisture, solar radiation, pear internal temperature, air velocity and mass loss of pear were measured at 30 min intervals. Experiments were carried out during the periods of June 2017 in Elazig, Turkey. The experiments started at 8:00 a.m. and continued till 18:00. The experiments were continued until the weight changes in the pear slices stopped. Wet basis moisture content (MCw), dry basis moisture content (MCd), adjustable moisture ratio (MR), drying rate (DR), and convective heat transfer coefficient (hc) were calculated with both in the AHSC dryer and the open sun drying experiment data. It was found that the values of hc in both drying systems with a range 12.4 and 20.8 W/m2 °C. Three different kernel models were used in the support vector machine (SVM) regression to construct the predictive model of the calculated hc values for both systems. The mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative absolute error (RRAE) analysis were performed to indicate the predictive model's accuracy. As a result, the rate of drying of the pear was examined for both systems and it was observed that the pear had dried earlier in the AHSC drying system. A predictive model was obtained using the SVM regression for the calculated hc values for the pear in the AHSC drying system. The normalized polynomial kernel was determined as the best kernel model in SVM for estimating the hc values.