Comparison of Seven Numerical Methods for Estimating Weibull Parameters for Wind Energy Applications

Teyabeen, Alhassan Ali; Akkari, Fathi; Jwaid, Ali

doi:10.1109/uksim.2017.31

Cited by 13 publications

(11 citation statements)

References 13 publications

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“…MM is applied on the basis of the mean and standard deviation of the Weibull distribution (Akdağ & Dinler, 2009;Arslan et al, 2014;Chang, 2011;Costa Rocha et al, 2012;Ouahabi et al, 2020;Teyabeen et al, 2017;Wang et al, 2016).…”

Section: Moment Methods (Mm) or Standard Deviation Methodsmentioning

confidence: 99%

“…This method proposed by Justus et al, uses the mean wind speed and the standard deviation for estimating parameter k (Chang, 2011;Costa Rocha et al, 2012;Khchine et al, 2019;Li et al, 2020;Mohammadi et al, 2016;Ouahabi et al, 2020;Teyabeen et al, 2017;Werapun et al, 2015), through the following expression (Eq. 7).…”

Section: Empirical Methods Of Justus (Emj)mentioning

confidence: 99%

“…Their results indicated that the EEM is an efficient method for estimating Weibull parameters. Likewise, Teyabeen et al in their study on the comparison of seven numerical methods (GM, MM, EMJ, EML, EPFM, MLM, MMLM) in order to determine which is the most efficient in the evaluation of the parameters of Weibull distribution on the basis of the 1-year wind speed data, collected in Zuwara (Libya) (Teyabeen et al, 2017). His results indicate that the MLM give the best performance followed by EMJ and EML while the GM shows poor performance (Teyabeen et al, 2017).…”

Section: Wind Power Density Functionmentioning

confidence: 99%

“…Likewise, Teyabeen et al in their study on the comparison of seven numerical methods (GM, MM, EMJ, EML, EPFM, MLM, MMLM) in order to determine which is the most efficient in the evaluation of the parameters of Weibull distribution on the basis of the 1-year wind speed data, collected in Zuwara (Libya) (Teyabeen et al, 2017). His results indicate that the MLM give the best performance followed by EMJ and EML while the GM shows poor performance (Teyabeen et al, 2017). In the study did by Werapun et al in Alberta (Canada), these k and c parameters were determined using some methods EMJ, EPFM, MLM, MMLM and GM from 3 years data.…”

Section: Wind Power Density Functionmentioning

confidence: 99%

See 3 more Smart Citations

Influence of Weibull parameters on the estimation of wind energy potential

Aziz

Tsuanyo

Nsouandele

et al. 2023

Sustainable Energy res.

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Wind potential estimation is generally evaluated using two-parameter (k, c) Weibull distribution. Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Relative Error (RE) are computed in order to comparatively analyse fourteen methods of determining Weibull parameters. They are the Graphical Method, the Standard Deviation Method, the Empirical Method of Justus, the Empirical Method of Lysen, the Energy Pattern Factor Method, the Maximum Likelihood Method, the Modified Maximum Likelihood Method, the Alternative Maximum Likelihood Method, the Least Square Method, the Weighted Least squares Method, the Curve Fitting Method, the Wind Variability Method, the Moroccan Method and the Median and Quartile Method. These methods have been applied on three different windy sites (slightly, moderately and very windy sites) with hourly wind data over a period of 10 years (2005–2014), measured at 10 m height. As a result, compared to the other methods, Energy Pattern Factor method is the more suitable method applicable to assess the Weibull parameters for all wind speeds. However, the values obtained from RMSE, R2 and RE tests revealed that the WVM and MoroM methods are not suitable while all other methods are acceptable for the estimation of k and c. parameters. The determination of the wind power density and the gap between the predicted standard deviation by each method and the measured standard deviation for all the sites highlighted the relevance of EPFM method and the others methods. Moreover, this work reveals that the Weibull shape factor k decrease with height above ground level, while that of the scale factor c increase with height.

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Section: Moment Methods (Mm) or Standard Deviation Methodsmentioning

confidence: 99%

Section: Empirical Methods Of Justus (Emj)mentioning

confidence: 99%

Section: Wind Power Density Functionmentioning

confidence: 99%

Section: Wind Power Density Functionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Weibull parameters on the estimation of wind energy potential

Aziz

Tsuanyo

Nsouandele

et al. 2023

Sustainable Energy res.

View full text Add to dashboard Cite

show abstract

“…This method works well in places with low wind speeds, but it is one of the worst choices for most studies. [31]. The following descriptions are the advantages and disadvantages of each existing method.…”

Section: Distribution and Methodsmentioning

confidence: 99%

Forecasting Weibull Parameters With a Novel Alternative Graphical Technique for Low Wind Speeds

et al. 2022

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This paper analyses wind speed estimation for Weibull distribution using various methods. According to a previous study, the existing methods primarily target areas with moderate to high-velocity rates, and Malaysia is a tropical country with pleasant breezes all year. As a result, this research aims to devise the most efficient method of forecasting wind speeds in low-speed areas. The researcher compared existing methods such as the Moment of Method, Empirical Method, Graphical Method, Maximum Likelihood Method and the newly proposed Alternative Graphical method. The finding indicates that the novel proposed approach, the Alternative Graphical Method, is superior regarding Goodness of Fit, Kolmogorov Smirnov and Chi-Square. For Kolmogorov Smirnov, the Alternative Graphical Method is 3.4 % better than the second-best method. At the same time, the usage of Chi-Square is again at a top position, with a 61 % disparity between it and the second and third best places. However, the Alternative Graphical Method is in second place for Anderson Darling, but the forecast performance with a minimum difference of 0.3 %. These findings imply that the Alternative Graphical Method capable of making more precise predictions than current methods.

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Weibull parameter estimation methods on wind energy applications - a review of recent developments

Bulut,

Bingöl

2024

Theor Appl Climatol

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Comparison of Seven Numerical Methods for Estimating Weibull Parameters for Wind Energy Applications

Cited by 13 publications

References 13 publications

Influence of Weibull parameters on the estimation of wind energy potential

Influence of Weibull parameters on the estimation of wind energy potential

Forecasting Weibull Parameters With a Novel Alternative Graphical Technique for Low Wind Speeds

Weibull parameter estimation methods on wind energy applications - a review of recent developments

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