Comparison approach for wind resource assessment to determine the most precise approach

The distribution models of wind speed data are essential to assess the potential wind speed energy because they decrease the uncertainty in estimating wind energy output. Therefore, before performing a detailed potential energy analysis, the precise distribution model for data relating to wind speed must be found. This research contains material from numerous goodness-of-fit tests, such as Kolmogorov–Simonov, Anderson–Darling, chi-square, root mean square error, Akaike information criterion, and Bayesian information criterion, which were combined finally to determine the wind speed of the best-fitted distribution. The suggested method collectively makes each criterion. This method was useful in statistically fitting 14 distribution models to wind speed data collected at four sites in Pakistan. The consequences show that this method provides the best source for selecting the most suitable wind speed statistical distribution. Also, the graphical representation is consistent with the analytical consequences. This research presents three estimation methods that can be used to calculate the different distributions used to estimate the wind. In the suggested maximum likelihood method, method of moments, and maximum likelihood estimation, the third-order moment used in the wind energy formula is a crucial function because it contributes to the precise estimate of wind energy. In order to prove the presence of the suggested method of moments, it was compared with well-known estimation methods, such as the method of linear moments and maximum likelihood estimation. In the relative analysis, given several goodness-of-fit tests, the presentation of the considered techniques is estimated based on the actual wind speed evaluated in different periods. The results show that the method of moments provides a more precise estimation than other commonly used methods for estimating wind energy based on the 14 distributions. Therefore, the method of moments can be a better technique for assessing wind energy.