Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites

Zhou, Jian; Erdem, Ergin; Gong, Lixin; Shi, Jing

doi:10.1016/j.enconman.2010.01.020

Cited by 147 publications

(85 citation statements)

References 30 publications

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“…However, in this study, the fit of the Weibull distribution is poor (D = 0.10) compared to many other theoretical distributions. These results are in accordance with similar studies where the GoF of various theoretical distributions to empirical Energies 2016, 9, 344 9 of 20 distributions was compared [59][60][61]. The Weibull distribution is not even the best-fitting two-parameter distribution, which is the lognormal distribution.…”

Section: Distribution Fittingsupporting

confidence: 80%

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Jung

2016

Energies

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Abstract:In this paper a methodology is presented that can be used to model the annual wind energy yield (AEY mod ) on a high spatial resolution (50 mˆ50 m) grid based on long-term (1979-2010) near-surface wind speed (U S ) time series measured at 58 stations of the German Weather Service (DWD). The study area for which AEY mod is quantified is the German federal state of Baden-Wuerttemberg. Comparability of the wind speed time series was ensured by gap filling, homogenization and detrending. The U S values were extrapolated to the height 100 m (U 100m,emp ) above ground level (AGL) by the Hellman power law. All U 100m,emp time series were then converted to empirical cumulative distribution functions (CDF emp ). 67 theoretical cumulative distribution functions (CDF) were fitted to all CDF emp and their goodness of fit (GoF) was evaluated. It turned out that the five-parameter Wakeby distribution (WK5) is universally applicable in the study area. Prior to the least squares boosting (LSBoost)-based modeling of WK5 parameters, 92 predictor variables were obtained from: (i) a digital terrain model (DTM), (ii) the European Centre for Medium-Range Weather Forecasts re-analysis (ERA)-Interim reanalysis wind speed data available at the 850 hPa pressure level (U 850hPa ), and (iii) the Coordination of Information on the Environment (CORINE) Land Cover (CLC) data. On the basis of predictor importance (PI) and the evaluation of model accuracy, the combination of predictor variables that provides the best discrimination between U 100m,emp and the modeled wind speed at 100 m AGL (U 100m,mod ), was identified. Results from relative PI-evaluation demonstrate that the most important predictor variables are relative elevation (Φ) and topographic exposure (τ) in the main wind direction. Since all WK5 parameters are available, any manufacturer power curve can easily be applied to quantify AEY mod .

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Section: Distribution Fittingsupporting

confidence: 80%

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

Jung

2016

Energies

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“…Stathopoulos et al [65] used both numerical and statistical models for wind power prediction. Zhou et al [66] comprehensively evaluated wind speed distribution models for a case study of North Dakota country sites. Wind energy estimation and analysis of wind regions through the Weibull distribution methods is widely used nowadays [35,37,[67][68][69][70][71][72][73][74][75][76][77][78][79][80].…”

Section: Introductionmentioning

confidence: 99%

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

2014

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Abstract:The best Weibull distribution methods for the assessment of wind energy potential at different altitudes in desired locations are statistically diagnosed in this study. Seven different methods, namely graphical method (GM), method of moments (MOM), standard deviation method (STDM), maximum likelihood method (MLM), power density method (PDM), modified maximum likelihood method (MMLM) and equivalent energy method (EEM) were used to estimate the Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error (RMSE), mean percentage of error, mean absolute percentage of error, chi-square error and analysis of variance were used to precisely rank the methods. The statistical fittings of the measured and calculated wind speed data are assessed for justifying the performance of the methods. The capacity factor and total energy generated by a small model wind turbine is calculated by numerical integration using Trapezoidal sums and Simpson's rules. The results show that MOM and MLM are the most efficient methods for determining the value of k and c to fit Weibull distribution curves.

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“…Along with the Weibull distribution the Rayleigh distribution is one of the most used statistical distributions in reliability theory; is a special case of Weibull distribution, where the shape parameter is equal to two, and the scale parameter is √ 2 times of the scale parameter of the corresponding Weibull distribution [21]. Other distribution that have been reported in literature are Lognormal distribution, which is a special form of the normal distribution, Gamma and Erlang distributions which uses exponential approximations, inverse Gaussian distribution is also used and is popular in meteorology studies, additionally other alternatives for wind speed analysis include Skewed generalized error distribution, Skewed t distribution and Burr distribution [24].…”

Section: Wind Speed Distribution Modelsmentioning

confidence: 99%

“…An important tool from probability theory is used to analyze the wind speed: probability density function (PDF), which is a function that describes the relative likelihood of a random variable to take on a given value [20]. The PDF of wind speed predominantly determines the performance of a wind energy system in a given location for the period involved [21], that depends on several factors like weather and topography of the place on. The following briefly describes the most widely accepted PDF's.…”

Section: Wind Speed Distribution Modelsmentioning

confidence: 99%

Overall Description of Wind Power

Lemus

Serna

2014

ing.cienc.

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Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites

Cited by 147 publications

References 30 publications

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

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

Overall Description of Wind Power

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