Comparative analysis of regional wind energy potential of turkey by using distrubution functions

Energy is one of the most important indicators of economic growth in modern societies. Using electrical energy in every aspect of life makes us more dependent to electrical energy day by day. In order to sustain human life and make its standards higher, required energy should be provided continuously and safely. Due to the rapid increase in population and industrialization, energy demand is constantly increasing. Due to the negative effects of conventional energy sources on the ecosystem in parallel with the rapidly increasing energy demand, the use of renewable and clean energy sources has gained importance in meeting the demand for electricity. Moreover, one of the biggest factors that prevent the economic development of our country is that we are dependent on foreign countries to a large extent in conventional energy sources. On the other hand, renewable energy sources are one of the most important factors that will help us reduce our country's dependence on foreign countries and the current account deficit. One of the most important renewable energy sources is wind energy systems. Before the installation of a wind energy conversion system in a region, the wind speed characteristic of that region should be determined and modeled. In this context, many different distribution functions are proposed in determining the wind speed characteristic of a region. In this paper, performance analysis was performed by using two-parameter Weibull function and Rayleigh distribution function for determination of wind speed characteristics. In this context, in the evaluation of the model performance, hourly wind speed data of 2018 at an altitude of 10 meters for 81 provinces in Turkey were used. The maximum likelihood method was used to determine the parameters of the distribution function and the Root Mean Square Error (RMSE) was calculated for the model performance criteria. Matrix Laboratory (MATLAB) program was used for all analyzes and wind speed data which are monthly and yearly were statistically modeled with Burr Type XII distribution function.