Using genetic algorithms for estimating Weibull parameters with application to wind speed

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.

Channel Parameter Estimation in Millimeter-Wave Propagation Environments Using Genetic Algorithm

“…The genetic algorithm (GA) [4] is the simplest EA applicable to several wireless communications problems, including joint channel and data estimation [5], user detection [6], and joint user detection and channel estimation [7], [8]. In terms of estimating wireless channel parameters, GA was used to estimate parameters in a multipath environment in [9], and has shown superior performance compared to traditional numerical methods for estimating Weibull parameters in [10]. Recent studies have also demonstrated the effectiveness of EAs in parameter estimation for α-κ-µ fading channels [11].…”