Wind speed distribution modeling for wind power estimation: Case of Agadir in Morocco

Tizgui, Ijjou; Guezar, Fatima El; Bouzahir, Hassane; Benaid, Brahim

doi:10.1177/0309524x18780391

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…It is this approximate distribution that is used in the turbine formulas instead of the empirical histogram. It greatly simplifies the calculation of wind speed behaviour characterization as well as the potential and performance of wind energy systems [14]. Therefore, it is very important to determine the most appropriate functions that offer the best adjustments to wind speed data.…”

Section: Modelling the Wind Speed Frequency Distributionmentioning

confidence: 99%

Assessing the Best Fit Probability Distribution Model for Wind Speed Data for Different Sites of Burkina Faso

Boro¹,

Thierry²,

Kieno³

et al. 2020

CJAST

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In order to estimate the power output of a wind turbine, optimise its sizing and forecast the economic rate of return and risks of a wind energy project, wind speed distribution modelling is crucial. For which, Weibull distribution is considered as one of the most acceptable model. However, this distribution does not fit certain wind speed regimes. The objective of this study is to model the frequency distribution of the three-hourly wind speed at ten sites of Burkina Faso. In this context, we compared the accuracy of five distributions (Weibull, Hybrid Weibull, Rayleigh, Gamma and inverse Gaussian) which gave satisfactory results in this field. The maximum likelihood method was used to fit the distributions to the measured data. According to the statistical analysis tools (the coefficient of determination and the root mean square error), it was found that the Weibull distribution is most suited to the Bobo, Dédougou, Ouaga and Ouahigouya sites. On the other hand, for the sites of Bogandé, Fada and Po, the hybrid Weibull distribution is the most suitable one. As to the inverse Gaussian distribution, it is the most suitable for the Boromo, Dori and Gaoua sites. In addition, the analysis focused on comparing the mean absolute error of the annual wind power density estimation using the distributions examined. The Hybrid Weibull distribution was found to have a minimal mean absolute error for most study sites.

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Section: Modelling the Wind Speed Frequency Distributionmentioning

confidence: 99%

Assessing the Best Fit Probability Distribution Model for Wind Speed Data for Different Sites of Burkina Faso

Boro¹,

Thierry²,

Kieno³

et al. 2020

CJAST

View full text Add to dashboard Cite

show abstract

“…Typically, the electrical power produced by the wind energy is modelled as a statistical distribution of the wind speed (WS) random variable (RV); this determines that the estimation of wind electrical energy production is strictly related to the accuracy of the adopted wind speed distribution [5][6][7][8][9][10][11][12][13][14][15]. In particular, [5][6][7][8] presents a thorough account of the basic WS forecasting methods, [9] illustrates also the comparison of various models in terms of goodness-of-fit, while [10][11][12][13][14] are especially devoted to some more recent developments, and [15] casts WS forecasting in the framework of hybrid wind power generation, also giving an account on the related issue of battery life. All these references, as well as all the studies on the subject, show that an accurate wind speed distribution modeling is the first step to achieve accurate wind energy production estimation.…”

Section: Introductionmentioning

confidence: 99%

The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and Its Bayes Estimation

2022

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This paper proposes the Compound Inverse Rayleigh distribution as a proper model for the characterization of the probability distribution of extreme values of wind-speed. This topic is gaining interest in the field of renewable generation, from the viewpoint of assessing both wind power production and wind-tower mechanical reliability and safety. The first part of the paper illustrates such model starting from its origin as a generalization of the Inverse Rayleigh model by means of a continuous mixture generated by a Gamma distribution on the scale parameter, which gives rise to its name. Moreover, its validity for interpreting different field data is illustrated resorting to real wind speed data. Then, a novel Bayes approach for the estimation of such extreme wind-speed model is proposed. The method relies upon the assessment of prior information in a practical way, that should be easily available to system engineers. The results of a large set of numerical simulations—using typical values of wind-speed parameters—are reported to illustrate the efficiency and the accuracy of the proposed method. The validity of the approach is also verified in terms of its robustness with respect to significant differences compared to the assumed prior information.

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“…Wind speed characteristics are analyzed in depth to improve the performance of the electricity production in specific locations [7]- [10]. A Weibull distribution with maximum likelihood, energy pattern factor, and R 2 were analyzed [11], comparing the fourth accuracy is a Weibull distribution, Rayleigh, Gamma, and lognormal [12], comparison W3, W2, Gamma, Lognormal has been analyzed [13], estimate wind speed using the Weibull distribution function [14].…”

Section: Introductionmentioning

confidence: 99%

Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model

Suwarno

Rohana

2021

IJPEDS

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The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (Cr) and modified Rayleigh scale factor (Cm) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R2), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.

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Wind speed distribution modeling for wind power estimation: Case of Agadir in Morocco

Cited by 11 publications

References 13 publications

Assessing the Best Fit Probability Distribution Model for Wind Speed Data for Different Sites of Burkina Faso

Assessing the Best Fit Probability Distribution Model for Wind Speed Data for Different Sites of Burkina Faso

The Compound Inverse Rayleigh as an Extreme Wind Speed Distribution and Its Bayes Estimation

Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model

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