Deep assessment of wind speed distribution models: A case study of four sites in Algeria

Aries, Nawel; Boudia, Sidi Mohammed; Ounis, Houdayfa

doi:10.1016/j.enconman.2017.10.082

Cited by 85 publications

(75 citation statements)

References 35 publications

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“…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. 13 Masseran proposed an integrated rank approach in determining the best model selection for wind speed data. 13 Masseran proposed an integrated rank approach in determining the best model selection for wind speed data.…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

“…The correction of MTTND gives rise to a slight increase in the absolute value of the mean error of the mean square wind speed (this error changes from a negative to a positive value), a substantial growth in the IQR 13 , and a quasi-invariance of the symmetry of the error distribution. The correction of MTTND gives rise to a slight increase in the absolute value of the mean error of the mean square wind speed (this error changes from a negative to a positive value), a substantial growth in the IQR 13 , and a quasi-invariance of the symmetry of the error distribution.…”

Section: Relative Error Of the Mean Square Wind Speedmentioning

confidence: 99%

“…12 Similarly, Weibull, gamma, inverse normal, lognormal, Gumbel, generalized extreme value, Nakagami, and generalized logistic distribution were directly compared in Algeria. 13 Masseran proposed an integrated rank approach in determining the best model selection for wind speed data. To show the effectiveness of this method, the lognormal, Weibull, Rayleigh, exponential, Burr, gamma, inverse Gaussian, and inverse gamma distributions were considered and applied to model the wind speed regimes in Malaysia.…”

Section: Single and Mixture Distributionsmentioning

confidence: 99%

“…For this reason, a box plot representation of the relative error was used. The fundamental parameters of a box plot are the mean value, the median value, and the interquartile range IQR 13 , between the first and third quartiles. The distance between the first and the second quartile IQR 12 and that between the second and the third quartile IQR 23 allow the identification if the error distribution is symmetric.…”

Section: Goodness-of-fit and Accuracy In The Estimation Of The Charmentioning

confidence: 99%

“…traced by the wind turbine (m 2 ) A I area subtended by the TND A II area subtended by the MTTND E a available energy (J)f (v) probability density function (s/m) f (V i ) discrete probability density distribution (s/m) F (v) cumulative density function g(v) corrective function of the CTND (s/m) h(v)corrective function of the CMTTND (s/m) IQR 12 interquartile range between the first and second quartile (m/s) IQR13 interquartile range between the first and third quartile (m/s) IQR23 interquartile range between the second and third quartile (m/s) K I extreme wind speed parameter of the CTND (m/s) K II extreme wind speed parameter of the CMTTND (m/s) l longitude (°) L latitude (°) N number of wind speed observations…”

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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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Section: Single and Mixture Distributionsmentioning

confidence: 99%

Section: Relative Error Of the Mean Square Wind Speedmentioning

confidence: 99%

Section: Single and Mixture Distributionsmentioning

confidence: 99%

Section: Goodness-of-fit and Accuracy In The Estimation Of The Charmentioning

confidence: 99%

mentioning

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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A new approach based on moving least square method for calculating the Weibull coefficients

Kaplan

2022

Env Prog and Sustain Energy

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In this study, the graphical method, which is widely used to find the coefficients of the Weibull distribution function (WDF), has been improved by using the moving least squares approximation (MLSA) replace least squares method (LSM). The accuracy of the results obtained by the moving least squares method is shown by two different error analysis tests. In order to implement this method, Osmaniye region, which is rich in wind potential maintenance, has been selected. The root mean square error (RMSE) and mean percentage error (MPE) are used to compare the efficiency of proposed method. In this study, the results of the graphical method developed by the MLSA were compared monthly and yearly with the results obtained according to the least squares method. It was observed that the results of the proposed method in the two error analysis tests were much better. Also, the mean wind speed and average wind power values of the selected region were calculated with the coefficients found by MLSA. These calculated values were compared with actual values.

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Comparison of probability distributions used for harnessing the wind energy potential: a case study from India

Gugliani,

Ley,

Nakhaei Rad

et al. 2024

Stoch Environ Res Risk Assess

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Modeling wind speed data is the prime requirement for harnessing the wind energy potential at a given site. While the Weibull distribution is the most commonly employed distribution in the literature and in practice, numerous scientific articles have proposed various alternative continuous probability distributions to model the wind speed at their convenient sites. Fitting the best distribution model to the data enables the practitioners to estimate the wind power density more accurately, which is required for wind power generation. In this paper we comprehensively review fourteen continuous probability distributions, and investigate their fitting capacities at seventeen locations of India covering the east and west offshore corner as well as the mainland, which represents a large variety of climatological scenarios. A first main finding is that wind speed varies a lot inside India and that one should treat each site individually for optimizing wind power generation. A second finding is that the wide acceptance of the Weibull distribution should at least be questioned, as it struggles to represent wind regimes with heterogeneous data sets exhibiting multimodality, high levels of skewness and/or kurtosis. Our study reveals that mixture distributions are very good alternative candidates that can model difficult shapes and yet do not require too many parameters.

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Deep assessment of wind speed distribution models: A case study of four sites in Algeria

Cited by 85 publications

References 35 publications

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

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

A new approach based on moving least square method for calculating the Weibull coefficients

Comparison of probability distributions used for harnessing the wind energy potential: a case study from India

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