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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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Particle swarm optimization (PSO) algorithm is a heuristic optimization technique based on colony intelligence, developed through inspiration from social behaviors of bird flocks and fish schools. It is widely used in problems in which the optimal value of an objective function is searched. Geometrically nonlinear analysis of trusses is a problem of this kind. The deflected shape of the truss where potential energy value is minimal is known to correspond to the stable equilibrium position of the system analyzed. The objective of this study is to explore the success of PSO using this minimum total potential energy principle, in finding good solutions to geometrically nonlinear truss problems. For this purpose analyses are conducted on three structures, two plane trusses and a space truss. The results obtained show that in case of using 20 or more particles, PSO produces very good and robust solutions.

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