Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

Azad, A.K.; Rasul, M.G.; Yusaf, Talal

doi:10.3390/en7053056

Cited by 157 publications

(86 citation statements)

References 95 publications

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“…For this reason, according to the results of the studies, it might be concluded that suitability of the method may vary with the sample data size, sample data distribution, sample data format and goodness of fit tests [24]. Research of Aazad et al shows MOMs to be the most efficient method for determining the value of k and c to fit the Weibull distribution curves at any altitude [25]. This research proves to provide better information than others as 7 methods for parameter evaluation are compared at different altitudes.…”

Section: Wind Speed Probability Distributionmentioning

confidence: 99%

“…To evaluate the performance of considered distribution, the mean percentage error (MPE), mean absolute percentage error (MAPE), root mean square error (RMSE) parameter, and the chi-square test are performed [25]. MPE shows the average of percentage deviation between calculated value from weibull & Rayleigh distribution from the observed value whereas MAPE shows average absolute percentage deviation.…”

Section: Evaluation Of Weibull and Rayleigh Distributionsmentioning

confidence: 99%

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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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Section: Wind Speed Probability Distributionmentioning

confidence: 99%

Section: Evaluation Of Weibull and Rayleigh Distributionsmentioning

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 cumulated probability density, which is given in the following equation, is illustrated in the sixth column of the table [19,20]:…”

Section: Weibull Distribution Functionmentioning

confidence: 99%

Determination of Weibull Parameters by Different Numerical Methods and Analysis of Wind Power Density in Osmaniye, Turkey

Kaplan

2017

Scientia Iranica

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Abstract. In this study, the potential of wind energy in Osmaniye Region has been analysed statistically on the basis of information that has been measured hourly between the years 2009 to 2013. The two-parameter Weibull Distribution Function is generally applied to evaluate the potential of wind energy in any region. This study introduced the evaluation of Weibull Distribution Function parameters which are obtained by di erent kinds of numerical methods, namely Graphical Method (GM), Moment Method (MM), Energy Pattern Method (EPM), Energy Trend Method (ETM), and Maximum Likelihood Method (MLM). The Relative Percentage Error (RPE) of statistical test is used to compare the e ciency of all used methods. The calculated power density of all used numerical methods is a major key issue for suitability use of wind energy. The evaluation of Weibull parameters and wind power distribution play a crucial role in producing electricity from wind power. The results of the used methods are compared, and the obtained pre-research results show that the wind energy potential in Osmaniye Region is statistically suitable for electricity production.

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“…The maximum likelihood estimation procedure for the 2-parameter Weibull function in equation (9), Azad, Rasul, Yusaf (2014) has been an extensively used method for estimating the parameters of the Weibull distribution due to, among many others, this particular desirable property. The commonly used procedure to determine k is the Newton-Raphson routine on equation (9):…”

Section: Weibull Modelmentioning

confidence: 99%

Wind Energy Assessment at Bafoussam, Cameroon

Kisito¹,

Nfor²,

Yémélé³

et al. 2015

JSD

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Three-hourly wind speed data measured using the Beaufort scale at a height of 10m, from 6am to 6pm local time (5 periods per day), was obtained from the Bafoussam Airport. It was analyzed using the Weibull and Rayleigh probability density models and wind rose plots. It was determined that the lowest wind speeds (most calms) were observed during the first period (6am) and the highest at 3pm (fourth period). The very low morning wind speed adversely affected the daily mean wind speed. Better, but still poor, power density results were obtained at this fourth (3pm) period. The monthly and yearly mean speeds varied between 1.9 and 3.1m/s and with very low standard deviations. The wind rose plots also showed that all the significant winds fell in the first quadrant (NE) and predominantly on angle 10 o with some discernibly on 20 o and 30 o , only. Three goodness-of-fit tests: the chi square, coefficient of determination or R 2 and root mean square error, showed the Weibull to be a better fit to the wind regime than the Rayleigh model. The shape parameters were always greater than the scale parameters. Results show that, using the Weibull parameters, the power density of Bafoussam falls in the category 1 of the wind energy resource group and hence is not a very good wind energy exploitable candidate.

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Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

Cited by 157 publications

References 95 publications

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

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

Determination of Weibull Parameters by Different Numerical Methods and Analysis of Wind Power Density in Osmaniye, Turkey

Wind Energy Assessment at Bafoussam, Cameroon

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