In this study, the statistical analysis of wind power density and wind speed distribution parameters of the selected cities from seven region of Turkey was investigated using the hourly wind speed data measured by the Turkish State Meteorological Service between 2005 and 2014. The Weibull and Rayleigh distributions were used for modeling and the success of this modeling process was evaluated according to the criterias of R 2 , RMSE and 2. The Weibull parameters and the Rayleigh parameters were estimated analytically, and the mean wind speed and energy potential were determined based on these parameters. At the Weibull distribution, the lowest mean wind speed and power density was obtained as 1.73 m/s and 5.78 W/m 2 in Adıyaman, respectively. The highest mean speed and power density was determined as 2.95 m/s and 33.32 W/m 2 in Sinop. At the Rayleigh distribution, the lowest and the highest mean speed and the power density was obtained as 1.72 m/s and 5.63 W/m 2 in Adıyaman, 3.06 m /s and 33.44 W/m 2 in Sinop, respectively. Generally, the highest mean wind speed and power density values were determined in Sinop, and the lowest mean wind speed and power density values in Adıyaman. According to statistical criteria in wind data analysis of Turkey, the Weibull distribution was better than the Rayleigh distribution.
• Wind Energy potential • Weibull distribution • Support vector machine In this study, wind energy potential of Sinop and Adıyaman provinces in different regions of Turkey were analyzed statistically, based on the hourly measured data by Directorate of State Meteorological Station in 2008-2017 years. Wind energy potential was determined by a distribution function. A predictive model has been obtained for the computed wind power values using an artificial intelligence method. The process diagram of study was shown in Figure A.
Statistical analysis of wind power density and wind speed distribution parameters in Elazig province Determination of wind energy potential using Weibull and Rayleigh distributions Change of wind speed with probability density function and cumulative density function Wind speed is not constant in each region and varies depending on surface and weather. Making appropriate and accurate predictions for wind is important in changing energy markets in recent years. There are two solution methods for predictions. These are increasing the number of measurements or getting characteristic of wind speed in the region with statistical methods. In this study, the statistical analysis of wind power density and wind speed distribution parameters in Elaziğ province was investigated using the hourly wind speed data measured by the General Directorate of Meteorology between 2005 and 2014. Weibull and Rayleigh distributions were used for modeling and the success of this modeling process was evaluated according to the parameters of R 2 , RMSE and 2. According to statistical criteria in wind data analysis of Elaziğ province, Weibull distribution is better than Rayleigh distribution (Table A). Table A. R 2 , RMSE and 2 values according to Weibull and Rayleigh distributions Years Weibull Distribution Rayleigh Distribution R 2 RMSE 2 R 2 RMSE 2 2005 0,927619 Purpose: The aim of this study is to determine the wind energy potential of Elazig province by using Weibull and Rayleigh distributions. Theory and Methods: The probability density distributions and power density were derived from time series data. Weibull and Rayleigh probability density function have been fitted to the measured probability distributions. The wind power density has been evaluated. Results: The Weibull model is generally better in fitting the measured yearly probability density distributions than the Rayleigh model, to the statistical criteria such as R 2 , RMSE and 2. Conclusion: It was concluded that it would be better to use the Weibull distribution in the analysis of wind data of Elaziğ.
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