A comparative study of five numerical methods for the estimation of Weibull parameters for wind energy evaluation at Eastern Jerusalem, Palestine

Abstract

Wind power provides a clean and feasible solution to generate electricity. The development of wind power applications requires a deep analysis of wind profiles and an accurate prediction of wind energy at a study site. This work explores the distribution of wind speed to estimate the two Weibull parameters (shape and scale) that are widely utilized for modeling and providing an accurate and efficient estimation of wind resource and power. These two parameters are calculated based on measured daily wind speed data from 2008 to 2018, collected in Jerusalem, Palestine. Three assessment criteria were used to assess the goodness of fit; they are Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and chi-square. The findings revealed that of the five estimation methods being considered in this study, both the Empirical Method (EM) and the Method of Moment (MoM) were the most accurate in determining the values of the Weibull shape and scale parameters to approximate wind speed distribution at the study site. Based on the goodness-of-fit tests, both methods provide lower values of the used assessment criteria. The statistical performance tests rejected the Energy Pattern Factor Method (EPFM) as an adequate method due to the higher values of chi-square and revealed that the Maximum Likelihood (MLM) and the Modified Maximum Likelihood (MMLM) methods ranked third and fourth, respectively.