A comparative analysis of wind power density prediction methods for Çanakkale, Intepe region, Turkey

Oner, Y.; Ozcira, Selin; Bekiroglu, Nur; Şenol, İbrahim

doi:10.1016/j.rser.2013.01.052

Cited by 26 publications

(13 citation statements)

References 23 publications

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“…Given that R 2 value can be maximum 1.0000, it is understood that Gamma distribution shows very high sensitivity with this value of 2016. While there are results which support our study in the literature [18], there are also studies in which Weibull distribution yielded better results [1,7,[19][20][21][22].…”

Section: Weibull Lognormal Gammasupporting

confidence: 89%

Statistical Analysis of Wind Speed Data with Weibull, Lognormal and Gamma Distributions

Akyüz¹,

Gamgam²

2017

Cumhuriyet Science Journal

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In this study, average wind speed data in Bitlis for the years between 2012 and 2016 is analyzed. Average wind speed estimations for these years are obtained with the Weibull, Gamma and Lognormal distributions. Maximum Likelihood method is used in parameter estimation of these distributions. It is aimed that the most fit distribution is determined with Kolmogorov-Smirnov Goodness of Fit test, coefficient of determination and root mean square error criteria. As a result of evaluating the wind speed data with the program written in MATLAB R2009a, it was determined that average wind speed estimations are similar for each distribution, but Gamma distribution has the lowest standard deviation with the average wind speed value in August (0.15 m/s). In modelling of the average wind speed data between 2012 and 2016, it was seen that Gamma distribution had higher coefficient of determination compared to the other distributions. Similarly, the lowest Kolmogorov-Smirnov Goodness of Fit test statistic and root mean square error value are obtained for Gamma distribution. As a result, it is recommended that Gamma distribution is used in modelling the wind speed data of Bitlis between 2012 and 2016.

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Section: Weibull Lognormal Gammasupporting

confidence: 89%

Statistical Analysis of Wind Speed Data with Weibull, Lognormal and Gamma Distributions

Akyüz¹,

Gamgam²

2017

Cumhuriyet Science Journal

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“…They presented the site falls under class 7, indicating possibility of large scale electricity generation. Oner et al have studied about Weibull distribution, Rayleigh distribution & normal distribution and used them to study potential turbine locations [9]. For similar analysis, Odo et al [10] in Enugu, Nigeria; Ahmed et al [11] in Halabja, Iraq; Islam et al [12] in Kudat and Labuan, Malaysia; Safari et al [13] in Rwanda; Oyedepo et al [14] in south-east Nigeria; Abbas et al [15] in Pakistan and many other researchers have used Weibull distribution & Rayleigh Distribution in different places for analysing wind speed data.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

Parajuli¹

2016

EPE

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“…The Weibull probability density function f (v) and Weibull cumulative distribution function F(v) are expressed as follows [11][12][13][14][15][16]:…”

Section: Weibull Distribution Function and Estimation Methodsmentioning

confidence: 99%

Evaluation of Wind Energy Potential Using an Optimum Approach based on Maximum Distance Metric

et al. 2020

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The integration of wind power as an alternative energy source has gotten much attention globally. In this paper, the Weibull distribution model based on different artificial intelligent algorithms and numerical methods is used to evaluate the wind profile. The application of Weibull distribution in wind data assessment can be extensively found, but the methods applied for estimating the parameters still need improvement. Three artificial intelligent algorithms are presented as an alternative method for estimation of Weibull parameters, and an objective function is proposed through the concept of maximum distance metric. Its convergence was proven mathematically through its boundedness for all wind data types. The optimization methods based on the proposed objective function are compared with the conventional numerical approaches for Weibull parameter estimation. Two-year wind data from the site in the southern area of Pakistan has been used to conduct this analysis. Furthermore, this work provides an eloquent way for the selection of a suitable area, evaluation of parameters, and appropriate wind turbine models through real-time data for power production.

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A comparative analysis of wind power density prediction methods for Çanakkale, Intepe region, Turkey

Cited by 26 publications

References 23 publications

Statistical Analysis of Wind Speed Data with Weibull, Lognormal and Gamma Distributions

Statistical Analysis of Wind Speed Data with Weibull, Lognormal and Gamma Distributions

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

Evaluation of Wind Energy Potential Using an Optimum Approach based on Maximum Distance Metric

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