Energies 2014, 7(5), 3056-3085; doi:10.3390/en7053056
Article

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

1,* email, 1email and 2email
Received: 20 February 2014; in revised form: 22 April 2014 / Accepted: 23 April 2014 / Published: 2 May 2014
(This article belongs to the Special Issue Renewable Energy for Agriculture)
View Full-Text   |   Download PDF [588 KB, uploaded 2 May 2014]
Abstract: The best Weibull distribution methods for the assessment of wind energy potential at different altitudes in desired locations are statistically diagnosed in this study. Seven different methods, namely graphical method (GM), method of moments (MOM), standard deviation method (STDM), maximum likelihood method (MLM), power density method (PDM), modified maximum likelihood method (MMLM) and equivalent energy method (EEM) were used to estimate the Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error (RMSE), mean percentage of error, mean absolute percentage of error, chi-square error and analysis of variance were used to precisely rank the methods. The statistical fittings of the measured and calculated wind speed data are assessed for justifying the performance of the methods. The capacity factor and total energy generated by a small model wind turbine is calculated by numerical integration using Trapezoidal sums and Simpson’s rules. The results show that MOM and MLM are the most efficient methods for determining the value of k and c to fit Weibull distribution curves.
Keywords: the Weibull shape factor; scale factor; probability density function; power density; statistical tools
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Azad, A.K.; Rasul, M.G.; Yusaf, T. Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications. Energies 2014, 7, 3056-3085.

AMA Style

Azad AK, Rasul MG, Yusaf T. Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications. Energies. 2014; 7(5):3056-3085.

Chicago/Turabian Style

Azad, Abul K.; Rasul, Mohammad G.; Yusaf, Talal. 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications." Energies 7, no. 5: 3056-3085.


Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert