Assessing the performance of several numerical methods for estimating
Weibull parameters for Wind Energy Applications: A case study of
Al-Hodeidah in Yemen
Abstract
For the aim of determining the wind speed characteristics and wind power
density, the performance of five numerical approaches to identify the
shape (k) and scale (c) parameters of the Weibull distribution function
is assessed in this work. The chosen methods are the empirical Justus
method (EMJ), the maximum likelihood method (ML), the energy pattern
factor method (EPF), the moment method (MOM), and L-moment estimation
method (L-MOM). In order to calculate the mean wind speed, wind speed
standard deviation, and wind power density for Al-Hodeidah in West
Yemen, the best suitable method must be determined. Daily mean wind
speeds collected from January to December 2014 are used in this study.
The findings show that using various parameter estimating techniques
alters the precision of the values derived for mean wind speed, mean
wind power density and their standard deviation, skewness and kurtosis.
For this site, all of the methods: EMJ, EPF, ML, MOM, and L-MOM present
a very good accuracy for predicting mean wind speed on both daily and
monthly basis. For forecasting standard deviation of wind speed, the EMJ
method performs the best one on both daily and monthly scales. The ML
method is recommended for assessing the wind energy potential since it
presents better performance in terms of forecasting the daily and
monthly average power density at the study site. All methods showed a
notable error with relative percent error (RPE) larger than 10% at the
study site for the skewness and kurtosis of both wind speed and power
density except the EPM method is sufficient for predicting the kurtosis
of power with relative percent errors (RPE) smaller than 3%. For the
standard deviation of power density analyses, all of the methods show
remarkable errors with relative percent errors (RPE) greater than 11%.