Analysis and Comparison of Weibull Parameters for Wind Energy Potential Using Different Estimation Methods: A Case Study of Isparta Province in Turkey

被引:3
作者
Bulut, Aydin [1 ]
Bingoel, Okan [1 ]
机构
[1] Isparta Univ Appl Sci, Dept Elect & Elect Engn, Isparta, Turkiye
关键词
Weibull distribution; wind energy; numerical methods; MRFO; statistical analysis; GUI; NUMERICAL-METHODS; RESOURCES;
D O I
10.1080/15325008.2023.2210574
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this study, the success of the numerical methods and a metaheuristic algorithm in parameter estimation of Weibull distribution, which is frequently used in wind energy applications, are compared. Numerical methods are Justus empirical method, moment method, graphical method, energy pattern factor method (EPM), energy trend method, maximum likelihood estimation method (MLE). The metaheuristic algorithm is manta ray foraging optimization method (MRFO). The wind data used in the study were recorded hourly in the Isparta region in the southwest of Turkey. A graphical user interface design has been made to easily perform the calculations in this study. The success of the methods was tested with four different statistical error analysis methods. According to the results of the analysis, the MRFO method was by far the most successful method. EPM and MLE methods were the most unsuccessful methods.
引用
收藏
页码:1829 / 1845
页数:17
相关论文
共 41 条
[1]   Comparison between two new censored regression models extended from Burr-XII system with application [J].
Abdulkareem, Anwaar Dhiaa ;
Mohammed, Sunbul Rasheed .
INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 2022, 13 (01) :3395-3403
[2]   A new method to estimate Weibull parameters for wind energy applications [J].
Akdag, Seyit A. ;
Dinler, Ali .
ENERGY CONVERSION AND MANAGEMENT, 2009, 50 (07) :1761-1766
[3]   A comparative study of five numerical methods for the estimation of Weibull parameters for wind energy evaluation at Eastern Jerusalem, Palestine [J].
Alsamamra, Husain R. ;
Salah, Saeed ;
Shoqeir, Jawad A. H. ;
Manasra, Ali J. .
ENERGY REPORTS, 2022, 8 :4801-4810
[4]   Comparative study of numerical methods for determining Weibull parameters for wind energy potential [J].
Arslan, Talha ;
Bulut, Y. Murat ;
Yavuz, Arzu Altin .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2014, 40 :820-825
[5]   Analysis of wind energy prospect for power generation by three Weibull distribution methods [J].
Azad, A. K. ;
Rasul, M. G. ;
Islam, Rubayat ;
Shishir, Imrul R. .
CLEAN, EFFICIENT AND AFFORDABLE ENERGY FOR A SUSTAINABLE FUTURE, 2015, 75 :722-727
[6]   Wind power characteristics of seven data collection sites in Jubail, Saudi Arabia using Weibull parameters [J].
Baseer, M. A. ;
Meyer, J. P. ;
Rehman, S. ;
Alam, Md. Mahbub .
RENEWABLE ENERGY, 2017, 102 :35-49
[7]   Integrated technical analysis of wind speed data for wind energy potential assessment in parts of southern and central Nigeria [J].
Ben, Ubong C. ;
Akpan, Anthony E. ;
Mbonu, Charles C. ;
Ufuafuonye, Chika H. .
CLEANER ENGINEERING AND TECHNOLOGY, 2021, 2
[8]   Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function [J].
Bilir, Levent ;
Imir, Mehmet ;
Devrim, Yilser ;
Albostan, Ayhan .
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2015, 40 (44) :15301-15310
[9]   Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region [J].
Carneiro, Tatiane C. ;
Melo, Sofia P. ;
Carvalho, Paulo C. M. ;
Braga, Arthur Plinio de S. .
RENEWABLE ENERGY, 2016, 86 :751-759
[10]  
Celeska M., 2015, P EUROCON 2015, P1, DOI [10.1109/EUROCON.2015.7313684, DOI 10.1109/EUROCON.2015.7313684]