Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites

被引:0
作者
Kaplan, Yusuf Alper [1 ]
机构
[1] Osmaniye Korkut Ata Univ, Fac Engn, TR-80000 Osmaniye, Turkiye
来源
GAZI UNIVERSITY JOURNAL OF SCIENCE | 2024年 / 37卷 / 01期
关键词
Weibull distribution function; Maximum likelihood method; WE potential; NUMERICAL-METHODS; WIND CHARACTERISTICS; PARAMETERS; DISTRIBUTIONS;
D O I
10.35378/gujs.1092617
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In this study, the compliance of the Weibull Distribution Function (WDF) and actual wind data (WD) from three different locations were investigated. The coefficients of the WDF were calculated using the Maximum Likelihood Method (MLM) in the Adana, Osmaniye, and Hatay regions. The main purpose of this study is to observe the performances of the MLM in determining the coefficients of the WDF in different regions in different years and to examine the success of this method in estimating the mean wind power and speed of the determined regions. The performance of the indicated approach in all three selected locations was evaluated using the Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Mean Percentage Error (MPE). Also wind power densities were estimated for all three regions, which are one of the most essential metrics for estimating a region's wind energy (WE) potential. WDF power densities were estimated and compared to real wind power densities generated from measured WD for three different places. The performance of the method described in this paper was investigated in depth in various places with varying geographic characteristics. In addition, in the same years, the performance of the chosen method was evaluated in detail in three distinct places, and it was seen how geographical factors affected the method's performance.
引用
收藏
页码:237 / 247
页数:11
相关论文
共 28 条
[21]   Wind energy for sustainable development: Driving factors and future outlook [J].
Sadorsky, Perry .
JOURNAL OF CLEANER PRODUCTION, 2021, 289
[22]   Wind energy potential assessment using Weibull distribution with various numerical estimation methods: a case study in Mersing and Port Dickson, Malaysia [J].
Safari, Muhammad Aslam Mohd ;
Masseran, Nurulkamal ;
Majid, Muhammad Hilmi Abdul .
THEORETICAL AND APPLIED CLIMATOLOGY, 2022, 148 (3-4) :1085-1110
[23]   Evaluation of wind power potential in Baburband (Pakistan) using Weibull distribution function [J].
Shoaib, Muhammad ;
Siddiqui, Imran ;
Amir, Yousaf Muhammad ;
Rehman, Saif Ur .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 70 :1343-1351
[24]   Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution [J].
Soulouknga, M. H. ;
Doka, S. Y. ;
Revanna, N. ;
Djongyang, N. ;
Kofane, T. C. .
RENEWABLE ENERGY, 2018, 121 :1-8
[25]  
TEIAS, Turkish Electricity Transmission CompanyTurkiye Elektrik Iletim A.S
[26]   A new estimation approach based on moments for estimating Weibull parameters in wind power applications [J].
Usta, Ilhan ;
Arik, Ibrahim ;
Yenilmez, Ismail ;
Kantar, Yeliz Mert .
ENERGY CONVERSION AND MANAGEMENT, 2018, 164 :570-578
[27]   An innovative estimation method regarding Weibull parameters for wind energy applications [J].
Usta, Ilhan .
ENERGY, 2016, 106 :301-314
[28]   Statistical analysis of low-occurrence strong wind speeds at the pedestrian level around a simplified building based on the Weibull distribution [J].
Wang, Wei ;
Okaze, Tsubasa .
BUILDING AND ENVIRONMENT, 2022, 209