Modelling Wind Energy Potential in Different Regions with Different Methods

Processing a lot of data is very difficult and laborious task. In order to save time ease the process, computational intelligence method practical for processing. present study, potential wind energy in different regions Turkey based on hourly speed years 2008-2017 were analysed statistically. Wind power density values have been examined mathematically statistically modelled using artificial methods. During statistical analysis, maximum speed, average density, standard deviation determined. The cumulative Weibull function was used determine distribution an annual basis data. Predictive models created by machine learning algorithms which are obtained values. Decision tree (DT) algorithm multilayer perceptron (MLP) chosen as algorithms. Four error analyses performed DT MLP estimates. estimate values, approximately 35% more accurate than algorithm. As result, densities certain determined both mathematical model