Mixture probability distribution functions using novel metaheuristic method in wind speed modeling

Wind speed modeling is essential for predicting wind energy potential and properly utilizing wind power. The efficiency of the chosen probability distribution function (PDF) to describe the measured wind speed frequency distribution determines the quality of the wind speed assessment. The original probability distribution functions are insufficient in wind speed modeling as they have a significant error between real wind speed frequency distribution and the estimated distribution curve, while the mixture probability distributions can enhance the fitting of wind speed frequency distribution and reduce this error. The objective of this work is to model wind speed characteristics using two and three components mixture probability distribution functions generated from the combination of original Weibull, Gamma, and Inverse Gaussian distribution functions. To examine the fitness of the probability distribution functions, statistical errors such as the root mean square error (RMSE), Chi-square error (X2), and coefficient of Correlation (R2) are used as judging criteria. Results indicate that the two-component mixture Weibull distribution gives the best fitting among the original and the other two-component mixture PDFs. Moreover, the results show that the three-component mixture Weibull distribution gives the best fitting among all distributions proposed in this work. The mixture distribution functions used in this study have a higher level of complexity than the original distribution in estimating parameters as they have a higher number of optimization variables that need an artificial intelligence optimization method. The novel metaheuristic Aquila Optimizer (AO) method is used to estimate the parameters of proposed original and mixture PDFs in this work.