Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis and wind energy potential assessments for a given location. Such assessments require the implementation of an accurate and suitable wind distribution model. Therefore, in the quest for a well-fitted model, eight methods for estimating the Weibull parameters are investigated in this paper. The methods were then investigated by employing statistical tools, and their performances have been discussed in terms of various error indicators such as root mean squared error (RMSE), regression error (R2), chi-square (X2), and mean absolute error (MAE). Meteorological data for diverse terrain from 14 provinces with 30 sites scattered across Iran were employed to examine the performance of the investigated methods. The results demonstrated that the empirical method has superiority over the investigated technique in terms of errors.

In recent years, electricity generation by employing wind turbines has been increased significantly due to technology advancement and environmental concern. In the present study, meteorological data have been employed to compute the Weibull distribution function as well as wind power and energy density. The wind data measured at 10-min intervals at three heights of 10, 30, and 40 m above the ground. Monthly, seasonal, and annual wind speed variations are analyzed, and the performance of the selected wind turbines is evaluated. The results show that the highest wind power potential occurs in July at Lotak and in August at Shandol. The estimated annual power and energy densities demonstrated that both investigated sites are suitable for large-scale power generation. Finally, the levelized cost of energy (LCOE) is employed to evaluate the economic feasibility of electricity generation from wind power energy. It was demonstrated that the minimum cost of energy achieved by Dongfang DF100-2500 model with LCOE of 0.0830 and 0.0786 USD/kWh in Lotak and Shandol, respectively.