In Turkey, many enterprisers started to make investment on renewable energy systems after new legal regulations and stimulus packages about production of renewable energy were introduced. Out of many alternatives, production of electricity via wind farms is one of the leading systems. For these systems, the wind speed values measured prior to the establishment of the farms are extremely important in both decision making and in the projection of the investment. However, the measurement of the wind speed at different heights is a time consuming and expensive process. For this reason, the success of the techniques predicting the wind speeds is fairly important in fast and reliable decision-making for investment in wind farms. In this study, the annual wind speed values of Kutahya, one of the regions in Turkey that has potential for wind energy at two different heights, were used and with the help of speed values at 10 m, wind speed values at 30 m of height were predicted by seven different machine learning methods. The results of the analysis were compared with each other. The results show that support vector machines is a successful technique in the prediction of the wind speed for different heights.

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Poly(2-hydroxyethyl methacrylate)-co-poly(4-vinyl pyridine) and poly(HEMA)-co-poly-(4-VP) copolymers were synthesized by free radical polymerization. K2S2O8 was used as an initiator. Chain lengths of the copolymer was changed by varying the monomer/initiator ratio. These polymers have molarites of 2.6 and 2.1 respectively and are called COP2 and COP4. The samples were exposed to gamma rays at room temperature. After irradiation, the EPR spectra of COP2 and COP4 were recorded between 120 K and 450 K. From the temperature dependence of the line intensity, it was concluded that unpaired spin concentration in the irradiated samples has been changing with temperature. A theoretical study, presented in this report, was aimed to test success of the machine learning methods and to select the best learning method.

The polycrystals of 2,4 diaminotoluene were produced by slow evaporation of solvent. The polycrystalline samples were exposed to 60 Co gamma rays with dose rate of 0.950 kGy/h, at room temperature, for 12, 24, 48, and 72 hours. The electron paramagnetic resonance measurements were carried out on these samples in the temperature range between 298 K and 400 K. No electron paramagnetic resonance signal was observed in the samples irradiated for 12, 24, 48 hours. Two types of radicals were detected using ESR spectrometer in the sample irradiated for 72 h. These radiation damage centers were called RI and RII. The average values of g and the hyperfine coupling constant were calculated. This study also investigates the potential usage of machine learning methods and aims to test the success of these methods and to select the best method.