An innovative estimation method regarding Weibull parameters for wind energy applications


ENERGY, vol.106, pp.301-314, 2016 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 106
  • Publication Date: 2016
  • Doi Number: 10.1016/
  • Journal Name: ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.301-314
  • Keywords: Weibull distribution, Parameter estimation, Probability weighted moments method, Probability weighted moments based on the power density method, Wind energy, SPEED DISTRIBUTION, NUMERICAL-METHODS, PROBABILITY-DISTRIBUTIONS, POWER, REGION, TURKEY, STATISTICS, GENERATORS, MODELS
  • Anadolu University Affiliated: Yes


Two-parameter Weibull distribution is widely-used and accepted as the most popular distribution for the evaluation of wind energy potential. However, it is well-known that parameter estimates have significant effects on the success of Weibull distribution for wind energy applications. Thus, in this paper, an innovative method, PWMBP (probability weighted moments based on the power density method), is developed and proposed for estimating the Weibull parameters in wind energy applications. The salient features of PWMBP compared to other common methods are presented and discussed. In particular, this method is expressed in analytical form and does not need extensive numerical calculations or any iterative procedure. Moreover, the performance of PWMBP is compared with six other commonly-used methods; the maximum likelihood, modified maximum likelihood, graphical, moment, power density and probability weighted moments methods for actual wind data, based on different time periods and regions according to various goodness of fit criteria, moreover energy output performance of these methods are examined. The obtained results indicate that PWMBP provides more accurate and efficient estimation than other methods in estimating the parameters of Weibull distribution. As a result, PWMBP can be used as an improved method to estimate the Weibull parameters for wind energy applications. (C) 2016 Elsevier Ltd. All rights reserved.