Statistical analysis of wind energy potential on the basis of the Weibull and Rayleigh distributions for Agin-Elazig, Turkey

Abstract

In the present study, the wind energy potential of the region is statistically analysed on the basis of 5 year measured hourly time series wind speed data. The probability density distributions are derived from time series data, and distributional parameters are identified. Two probability density functions are fitted to the measured probability distributions on a yearly basis. The wind energy potential of the location is studied on the basis of the Weibull and the Rayleigh models.

The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria

“…The maps can be used to estimate the monthly mean available wind energy for a given wind rotor blade with sweep area A at any given site with a mean wind speed v m as [6,12,18]:…”

“…Wind speed modelling and mapping are essential prerequisites in the design and sitting of wind power plants. Therefore, efficient conversion and utilization of the wind energy resource require accurate and detailed knowledge of the wind characteristics of the location [5][6][7][8]. In different parts of the world, meteorological data including wind speed distribution is monitored on continuous basis through ground or satellite stations.…”

Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

“…These seasonal variations of the wind are much more predictable. Finally, the short term time scale variability covers time scales of minutes to seconds, also well known by the term "turbulence" and which is of critical interest in the wind turbine design process [17][18][19][20][21][22][23]. For more than half a century the Weibull distribution has attracted the attention of statisticians working on theory and methods as well as various fields of statistics [22,[24][25][26][27].…”

Statistical Modelling of Botswana’s Monthly Maximum Wind Speed using a Four-Parameter Kappa Distribution

“…For efficient wind power evaluation, it is essential to model wind speed using a suitable statistical distribution that is able to represent the observations accurately and the parameters of this distribution need to be estimated using an appropriate technique (Parida, 1999;Shabri and Jemain, 2010;Chiodo, 2013;Mukhopadhyay et al, 2014). Although researchers have suggested the use of Weibull distribution (Akpinar and Akpinar, 2004;Azad et al, 2014), this may not be applicable in semi-arid regions where wind speeds are highly variable. In view of this, a four parameter Kappa distribution with its parameters estimated using the L-moments method has been used to model Botswana's monthly maximum wind speeds.…”