Abstract
Renewable energy has become a prominent subject for researchers since fossil fuel reserves have been decreasing and are not promising to meet the energy demand of the future. Wind takes an important place in renewable energy resources and there is extensive research on wind speed modeling. Herein, one of the most commonly used distributions for wind speed modeling is the Weibull distribution with its simplicity and flexibility. Maximum likelihood (ML) method is the most frequently used technique in Weibull parameter estimation. Iterative techniques such as Newton-Raphson (NR) use random initial values to obtain the ML estimators of the parameters of the Weibull distribution. Therefore, the success of the iterative techniques highly depends on the initial value selection. In order to deliver a solution to the initial value problem, genetic algorithm (GA) is considered to obtain the estimators of the model parameters. The ML estimators obtained using the GA and NR techniques are compared with the method of moments (MoM) estimators via Monte Carlo simulation and wind speed applications. The results show that the ML estimators obtained using GA present superiority over MoM and the ML estimators obtained using NR.
Highlights
The increase in population and the inadequacy of existing energy resources put the human being into the search of alternative energy resources over the course of human history
To the best our knowledge, this is the first time genetic algorithm (GA) is used to estimate the parameters of Weibull distribution in wind speed distribution modeling
Best results are highlighted in bold. It is seen from the simulation results that the GA approach was more efficient than NR and MoM in the estimation of the shape and scale parameters according to Mean absolute error (MAE) and bias criteria
Summary
The increase in population and the inadequacy of existing energy resources put the human being into the search of alternative energy resources over the course of human history. Teimouri et al [13] compared their proposed L-moment estimator with several methods including the ML method, method of logarithmic moment, percentile method and MoM. Saleh et al [15] compared five different methods and recommended the mean wind speed method and the ML method for fitting Weibull distribution. GA presented a comparable good performance based on the maximization of the log-likelihood function With this motivation, the applicability of GA is used in wind speed data modeling. To the best our knowledge, this is the first time GA is used to estimate the parameters of Weibull distribution in wind speed distribution modeling. The remainder of this paper is structured as follows: Section 2 gives basic information about the Weibull distribution, Section 3 gives detailed information about the parameter estimation methods, Section 4 presents the simulation experiments and wind speed data analysis.
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