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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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Yüksek performanslı beton (high performance concrete, HPC)'un eksenel basınç dayanımının yüksek doğrulukla tahmini son derece önemli bir konudur. Geçtiğimiz yıllarda, çeşitli gelişmiş modelleme yaklaşımları ve metodolojileri kullanılarak farklı başarı oranları ile HPC basınç dayanımı tahminlemeleri uygulanmıştır. Bu çalışmada farklı karışım oranları kullanılarak HPC'lerin eksenel basınç dayanımının tahmininde uygun bir makine öğrenmesi yöntemi araştırılmıştır. Son yıllarda makine öğrenmesinde oldukça gelişmekte olan Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (DVM)'nin bu tahminde uygulanabilirliği incenmiş ve son derece yüksek tahmin sonuçları elde edilmiştir. Bu çalışmada DVM'lerin tahmin başarısının YSA'lara oranla daha tatmin edici sonuçlar verdiği görülmüştür. DVM yönteminin araştırma laboratuvarları ve beton firmaları tarafından dayanım tahmininde alternatif bir yöntem olarak etkin bir şekilde kullanılabileceği sonucuna varılmıştır.Highly accurate prediction of high performance concrete (HPC) compressive strength is very important issue. In recent years, a variety of modeling approaches and methodologies have been applied to predict HPC's compressive strength from a wide range of variables, with different ratios of success. In this study, an appropriate machine learning method, using different mixing ratios for the prediction of compressive strength of HPC, is investigated. In recent years, rather developing machine learning methods; Artificial Neural Networks (ANN) and Support Vector Machines (SVM)'s applicabilities for the prediction, handled in this study, are being investigated and extremely high results were obtained. In this paper, it's obtained that prediction success of SVM has been found more satisfactory than ANN's. It is concluded that the SVM's can be used effectively as an alternative method by research labs and the concrete firms for predicting the strength. Anahtar kelimeler: Yüksek performanslı beton, Yapay sinir ağları, Destek vektör makineleri

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The study investigates the accuracy of bagging ensemble models (i.e., bagged artificial neural networks (BANN) and bagged regression trees (BRT)) in monthly crude oil price forecasting. Two ensemble models are obtained by coupling bagging and two simple machine learning models (i.e., artificial neural networks (ANN) and classification and regression trees (CART)) and results are compared with those of the single ANN and CART models. Analytical results suggest that ANN based models (ANN & BANN) are superior to tree-based models (RT & BRT) and the bagging ensemble method could optimize the forecast accuracy of the both single ANN and CART models in monthly crude oil price forecasting.