Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network Algorithm

Harvesting the power coming from the wind provides a green and environmentally friendly approach to producing electricity. To facilitate the ongoing advancement in wind energy applications, deep knowledge about wind regime behavior is essential. Wind speed is typically characterized by a statistical distribution, and the two-parameters Weibull distribution has shown its ability to represent wind speeds worldwide. Estimation of Weibull parameters, namely scale $\left(c\right)$ and shape $\left(k\right)$ parameters, is vital to describe the observed wind speeds data accurately. Yet, it is still a challenging task. Several numerical estimation approaches have been used by researchers to obtain c and k. However, utilizing such methods to characterize wind speeds may lead to unsatisfactory accuracy. Therefore, this study aims to investigate the performance of the metaheuristic optimization algorithm, Neural Network Algorithm (NNA), in obtaining Weibull parameters and comparing its performance with five numerical estimation approaches. In carrying out the study, the wind characteristics of three sites in Saudi Arabia, namely Hafer Al Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA has high accuracy fitting results compared to the numerical estimation methods. The NNA demonstrates its efficiency in optimizing Weibull parameters at all the considered sites with correlations exceeding 98.54.