Errors in wind resource and energy yield assessments based on the Weibull distribution

Jourdier, Bénédicte; Drobinski, Philippe

doi:10.5194/angeo-35-691-2017

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…Its validity for application to wind speed modeling is argued and shown not to be the best to properly describe the wind speed characteristics of certain times and locations [8]. Some probability distributions that have also been deployed for assessment of wind speed include generalized extreme value distribution of Gumbel (Type I), Fréchet (Type II) and reverse Weibull (Type III) [6] and mixed Rayleigh-Rice distribution [12]. Other non-Weibull distribution applications are found in [22,16,25].…”

Section: Wind Speed Modelingmentioning

confidence: 99%

On Some Models for Wind Power Assessment in Yola, Nigeria

Esbond¹,

Saporu²

2021

IJSDA

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Probability distributions are used in the evaluation of wind energy potentials to describe the wind speed characteristics of the chosen location for wind farm establishment. However, the Weibull distribution that is the most chosen by wind energy modelers may likely fail to properly describe the wind speed data of certain locations, or it may not be the best model to describe wind speed when compared to the fitness of other probability distributions. Thus, in this study, four probability distributions are fitted to wind speed data from Yola, Nigeria. They are the Weibull, the exponentiated Weibull, the generalized power Weibull and the exponentiated epsilon distributions; and, all provided good fit to the wind speed dataset. The exponentiated epsilon distribution is new and provided the best fit. These models are compared based on the relative likelihood gain per data point; it is found that there is about 5% gain by the other three probability distributions over the Weibull distribution. Hence all the three distributions can also be used as wind models. The estimated average wind speeds computed using the four models at various hub heights show that wind is sufficiently available to support a wind turbine with a cut-in speed of 3 m/s at hub heights 90 m above ground level. For the exponentiated-epsilon model, average wind speed of 3.68 m/s at hub height of 120 m above ground level can generate 6.11 W/m 2 of electricity; and for a wind turbine of rotor diameter of 128 m with 12,868 m 2 swept area, this amounts to 78.6 kW of electricity supply for a small-scale wind power project. Consequently, Yola holds a good potential for the establishment of a wind farm.

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Section: Wind Speed Modelingmentioning

confidence: 99%

On Some Models for Wind Power Assessment in Yola, Nigeria

Esbond¹,

Saporu²

2021

IJSDA

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“…It is also observed that the Wind Atlas Analysis and Application Program (WAsP) (official website: http://www.WAsP.dk) method has a limited use in such studies. Most of the studies using the WAsP method through the WAsP software are not comparing it to other methods [34,35], but see [36]. In the current study, a selection of methods from the literature was used for the estimation of Weibull parameters..…”

Section: Distribution Analysismentioning

confidence: 99%

Comparison of Weibull Estimation Methods for Diverse Winds

Bingöl

2020

Advances in Meteorology

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Wind farm siting relies on in situ measurements and statistical analysis of the wind distribution. The current statistical methods include distribution functions. The one that is known to provide the best fit to the nature of the wind is the Weibull distribution function. It is relatively straightforward to parameterize wind resources with the Weibull function if the distribution fits what the function represents but the estimation process gets complicated if the distribution of the wind is diverse in terms of speed and direction. In this study, data from a 101 m meteorological mast were used to test several estimation methods. The available data display seasonal variations, with low wind speeds in different seasons and effects of a moderately complex surrounding. The results show that the maximum likelihood method is much more successful than industry standard WAsP method when the diverse winds with high percentile of low wind speed occur.

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“…The authors then demonstrate how this method can be used to add uncertainty information to economic feasibility studies. Jourdier and Drobinski (2017) note that wind resource assessments often make the assumption that wind speed distributions follow a Weibull curve, and they evaluate the error contained in that assumption. They use sample wind turbine power curves to demonstrate the important difference between an evaluation of the wind resource power as compared with the actual wind turbine output.…”

Section: Review Of Literaturementioning

confidence: 99%

Wind energy production uncertainty associated with wind assessments of various intervals

Aldeman

Loomis

2019

Wind Engineering

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While wind assessment periods commonly range from 1 to several years, this is typically based on experience and industry norms. In this investigation, we perform an analysis of the error that can be expected in a wind resource assessment of various lengths of time. In contrast to earlier work measuring the uncertainty of predicted wind speeds, the uncertainty in this evaluation is measured directly in terms of energy and revenue production. As the wind assessment period increased from 30 days to 1 year, the average error increased slightly. However, when the wind assessment period was increased to 2 years, the average error decreased significantly. Simultaneously, the standard deviation of the error distributions decreased and the magnitude of the maximum experimentally obtained error decreased. By understanding how the energy production uncertainty decreases with increasing assessment time, the length of the assessment period can be tailored to match a developer’s risk tolerance.

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Errors in wind resource and energy yield assessments based on the Weibull distribution

Cited by 13 publications

References 16 publications

On Some Models for Wind Power Assessment in Yola, Nigeria

On Some Models for Wind Power Assessment in Yola, Nigeria

Comparison of Weibull Estimation Methods for Diverse Winds

Wind energy production uncertainty associated with wind assessments of various intervals

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