Generalized Lindley and Power Lindley distributions for modeling the wind speed data. (15th November 2017)

• Record Type: Journal Article
• Authors: Arslan, Talha; Acitas, Sukru; Senoglu, Birdal
• Is Part Of: Energy conversion and management. Volume 152(2017)
• Page Start: 300
• Page End: 311
• Publication Date: 2017-11-15
• Subjects: Wind speed -- Weibull -- Generalized Lindley -- Power Lindley; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2017.08.017
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: Weibull distribution may be inadequate to model all wind speed data. GL provides the best fitting performance in most of the cases we consider. Weibull and PL produce the smallest PDE values in most of the considered cases. Abstract: In this study, we propose to use Generalized Lindley (GL) and Power Lindley (PL) distributions as an alternative to the Weibull distribution for modeling the wind speed data. In the application part of the study, we consider the actual wind speed data collected in hourly basis from the Bilecik, Bursa, Eskisehir and Sakarya sites, Turkey in 2009. These data sets are modeled by using GL and PL distributions. To compare their modeling performance with well known and widely used Weibull distribution, Weibull distribution is also included into the study. The results show that GL distribution provides the best fitting according to root mean square error (RMSE), coefficient of determination ( R 2 ), maximum value of the likelihood function corresponding to the ML estimates of the parameters ( ln L ) and Akaike information criterion (AIC). It is also seen that PL distribution is preferable in terms of power density error (PDE) criterion.