Evaluating the suitability of wind speed probability distribution models: A case of study of east and southeast parts of Iran

Alavi, Omid; Mohammadi, Kasra; Mostafaeipour, Ali

doi:10.1016/j.enconman.2016.04.039

Cited by 119 publications

(55 citation statements)

References 39 publications

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“…In this method, multiple numerical iterations are essential to determine two parameters of the Weibull function. The k and c parameters are calculated by solving two following equations [46]:…”

Section: Weibull Distribution Functionmentioning

confidence: 99%

Analysis of hydrogen production from wind energy in the southeast of Iran

Alavi

Mostafaeipour

Qolipour

2016

International Journal of Hydrogen Energy

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Section: Weibull Distribution Functionmentioning

confidence: 99%

Analysis of hydrogen production from wind energy in the southeast of Iran

Alavi

Mostafaeipour

Qolipour

2016

International Journal of Hydrogen Energy

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“…9 In another study, three PDFs, ie, Weibull, logistic, and lognormal, were compared to select the best one to model wind speed distribution in Inner Mongolia, China. 11 Instead, in another study, the extended generalized Lindley distribution was proposed and evaluated to show its capability in estimating wind speed data in different regions of Turkey compared with the Weibull, Rayleigh, lognormal, and gamma distributions. 11 Instead, in another study, the extended generalized Lindley distribution was proposed and evaluated to show its capability in estimating wind speed data in different regions of Turkey compared with the Weibull, Rayleigh, lognormal, and gamma distributions.…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

“…The analytical expression of the three-parameter function h(v) is obtained by substituting μ 2 and σ 2 to μ and σ in Equation (11). The analytical expression of the three-parameter function h(v) is obtained by substituting μ 2 and σ 2 to μ and σ in Equation (11).…”

Section: Corrected Mixture Of Two Truncated Normal Distributions (Cmentioning

confidence: 99%

“…10 Recently, Alavi et al studied the performance of Nakagami distribution against some previously used PDFs including exponential, Weibull, gamma, lognormal, loglogistic, inverse normal, and generalized extreme value in Iran. 11 Instead, in another study, the extended generalized Lindley distribution was proposed and evaluated to show its capability in estimating wind speed data in different regions of Turkey compared with the Weibull, Rayleigh, lognormal, and gamma distributions. 12 Similarly, Weibull, gamma, inverse normal, lognormal, Gumbel, generalized extreme value, Nakagami, and generalized logistic distribution were directly compared in Algeria.…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

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A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

2019

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Summary The use of wind speed probability density functions is a standard practice to represent different wind regimes. Generally, these regimes are distinguished by the following three characteristics: the shape of the distribution in the central wind speeds, amount of the calm wind speeds (CWS), and extreme wind speeds (EWS). An in‐depth review has highlighted that none of the parametric distributions available is suitable to represent the three main characteristics at the same time. To overcome this gap, the use of the corrected mixture of two truncated normal distributions (CMTTND) and corrected single truncated normal distribution (CTND) are proposed to represent, respectively, bimodal and unimodal wind speed distribution shapes. The CMTTND and CTND are obtained by introducing a correction, respectively, to the mixture of two truncated normal distributions (MTTND) and to the single truncated normal distribution (TND). The MTTND and TND permit an accurate representation of distributions with high levels of CWS. The CMTTND and CTND employ a new parameter, to accurately quantifying also the relative frequencies associated with EWS. The performance of the CMTTND and CTND was assessed using a goodness‐of‐fit (GOF) test and statistical measures of error in the evaluation of the characteristic mean wind speeds. The analytical expressions of these mean wind speeds are obtained and validated by a numerical integration method for the first time in this work. The accuracy of these distributions is compared with that of other conventional probability distribution models, of which three are unimodal and six bimodal, in four Italian locations and three American locations. The analysis of the results showed that the CTND and CMTTND allow obtaining high GOF of the experimental distributions with R2 and RMSE higher and lower than, respectively, 0.977 and 0.054. Moreover, the CTND results in the most accurate distribution in the estimation of the characteristic mean wind speeds in the case of localities with unimodal experimental distributions and the CMTTND in the case of localities with bimodal experimental distributions. Contrary to other distribution, CTND and CMTTND accuracies grow by increasing the grade of the characteristic mean wind speed by reaching also estimation values lower than 2% of the real ones. This is a great advantage in the wind energy source determination in a location since the available energy depends on the mean cubic wind speed.

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“…On the other hand, in the literature, although the most widely used statistical distribution for modelling the wind speed data is Weibull, it may not provide better fitting for all wind regimes. For this reason, different distributions are used for modelling the wind speed data (Brano et al, 2011;Kantar and Usta, 2015;Alavi et al, 2016;Jung et al, 2017). For example, Gumbel distribution has also been used to both estimate extreme wind speed required for the determination of the wind turbine class in the wind power industry and evaluate the wind energy potential required designing a wind turbine (Hong et al, 2013;Kang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Lower and Upper Quantiles of Gumbel Distribution: An Application to Wind Speed Data

Aydin¹

2018

Appl. Ecol. Env. Res.

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Aydin: Estimation of the lower and upper quantiles of Gumbel distribution: an application to wind speed data - Abstract. In this paper, we consider different estimators of the quantiles of two-parameter Gumbel distribution. We use methodologies known as maximum likelihood, modified maximum likelihood and probability weighted moment to obtain the estimators of the quantiles. We compare the performances of the estimators with respect to bias and mean square error criteria via Monte Carlo simulation study. Their robustness properties are also examined in the presence of data anomalies. In the real data analysis part of the study, the seasonal maximum daily wind speed data from Sinop station (Turkey) in 2015 is considered. It is modelled by using two-parameter Gumbel distribution and analysed to compare the performances of the methodology presented in the study. All in all, the results of simulations and the real data application show that the maximum likelihood and modified maximum likelihood estimators, which have similar performance, provide better performance than the probability weighted moment estimator does in both obtaining estimates of the quantiles of Gumbel distribution and modelling of the data for almost all cases.

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Evaluating the suitability of wind speed probability distribution models: A case of study of east and southeast parts of Iran

Cited by 119 publications

References 39 publications

Analysis of hydrogen production from wind energy in the southeast of Iran

Analysis of hydrogen production from wind energy in the southeast of Iran

A correction to the unimodal and bimodal truncated normal distributions for a more accurate representation of extreme and calm wind speeds

Estimation of the Lower and Upper Quantiles of Gumbel Distribution: An Application to Wind Speed Data

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