Performance analysis of Weibull methods for estimation of wind speed distributions in the adamaoua region of Cameroon

Kaoga, Dieudonné Kidmo; Yamigno, Serge Doka; Raïdandi, Danwé; Djongyang, Noël

doi:10.14419/ijbas.v3i3.3081

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…The comparison made in Table 1 is ranked according to the following criteria; i) WEE value should be closer to zero, ii) RMSE value should also be close to zero (Kidmo Kaoga et al, 2014) , iii) R 2 value should be closer to one (Akyuz and Gamgam, 2017). Table 2 presents the mean values which were calculated by using the data of Table 1.…”

Section: Resultsmentioning

confidence: 99%

Rüzgar Hızı Değerlendirmesinde İki Parametreli Weibull Dağılımı İçin Üç Parametre Tahmin Metodunun Karşılaştırmalı Performans Analizi

Onay

Dokur

Kurban

2021

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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The two-parameter Weibull distribution is widely used in the estimation of wind speed. In this paper, some recently proposed parameter estimation methods for the two parameter Weibull distribution have been compared using the wind data from two cities in Turkey. The compared methods in this paper are Wind Energy Intensification Method (WEIM), Energy Pattern Factor Method (EPFM), and Power Density Method (PD). These methods have been compared by checking the fit have provided in the probability-density graph. The comparison has also been made by comparing of efficiencies these methods in predicting wind energy density. Lastly, a comparison has been investigated by using the following performance criteria; Wind Energy Error (WEE), Root Mean Square Error (RMSE), Coefficient of Determination (R 2 ), and the results of error analysis have been compared. According to simulation results, while the EPFM and PD methods have shown a very close fit on the graph; when it comes to calculating wind energy densities, the PD method has shown a significant advantage over other methods. Finally, after the examination of error criteria, it was clear that EPFM has shown accurate performance which is the desired result. The main conclusion is that the accurate method for these selected regions is the EPFM.

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Section: Resultsmentioning

confidence: 99%

Rüzgar Hızı Değerlendirmesinde İki Parametreli Weibull Dağılımı İçin Üç Parametre Tahmin Metodunun Karşılaştırmalı Performans Analizi

Onay

Dokur

Kurban

2021

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…For WEE, it is desired to have a number that is the closest to zero. For RMSE, results close to zero mean more accuracy [24]. For the Coefficient of Determination method, it is considered that the higher the value of (R 2 ), the more accurate the result is [25].…”

Section: Coefficient Of Determination (R 2 )mentioning

confidence: 99%

Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

Onay

Dokur

Kurban

2021

ELEKTRON ELEKTROTECH

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To install a wind energy conversion system to a region, the wind speed characteristics of that region must be identified. The two-parameter Weibull distribution is highly efficient in modeling wind speed characteristics. In this study, the wind speed data of 32 cities in three different regions of Turkey have been comparatively analysed to estimate Weibull distribution function parameters by the use of three well-known methods (Graphical Method (GM), Maximum Likelihood Method (MLM), Justus Moment Method (JMM)) and three new parameter estimation methods (Energy Pattern Factor Method (EPFM), Wind Energy Intensification Method (WEIM), Power Density Method (PD)) which have been proposed in recent years. Three years of hourly wind speed data of the specified regions have been used. The performance metrics of these analyses have been compared using Wind Energy Error (WEE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). The results have shown that while the PD method has high model performance, the JMM is closely competitive with the MLM. Besides, the wind energy densities that were estimated by using actual data have been compared with the resulting Weibull distribution. It has been clear that the method that has the closest estimation to the actual values is the PD method.

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“…r gives some information about the goodness of fit of the chosen model. A higher r represents a better fit using the theoretical or empirical function, and the highest value is 1 [33]. The equation of r is presented as the following:…”

Section: The Coefficient Of Determinationmentioning

confidence: 99%

Influence of the ENSO and Monsoonal Season on Long-Term Wind Energy Potential in Malaysia

2018

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This paper assesses the long-term wind energy potential at three selected sites, namely Mersing and Kijal on the east coast of peninsular Malaysia and Kudat in Sabah. The influence of the El Niño-Southern Oscillation on reanalysis and meteorological wind data was assessed using the dimensionless median absolute deviation and wavelet coherency analysis. It was found that the wind strength increases during La Niña events and decreases during El Niño events. Linear sectoral regression was used to predict the long-term wind speed based on the 35 years of extended Climate Forecast System Reanalysis data and 10 years of meteorological wind data. The long-term monthly energy production was computed based on the 1.5 MW Goldwind wind turbine power curve. The measured wind data were extrapolated to the selected wind turbine default hub height (70 m.a.s.l) by using the site-specific power law indexed. The results showed that the capacity factor is higher during the Northeast monsoon (21.32%) compared to the Southwest monsoon season (3.71%) in Mersing. Moreover, the capacity factor in Kijal is also higher during the Northeast monsoon (10.66%) than during the Southwest monsoon (5.19%). However, in Kudat the capacity factor during the Southwest monsoon (36.42%) is higher compared to the Northeast monsoon (24.61%). This is due to the tail-effect of tropical storms that occur during this season in the South China Sea and Pacific Ocean.

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Performance analysis of Weibull methods for estimation of wind speed distributions in the adamaoua region of Cameroon

Cited by 7 publications

References 15 publications

Rüzgar Hızı Değerlendirmesinde İki Parametreli Weibull Dağılımı İçin Üç Parametre Tahmin Metodunun Karşılaştırmalı Performans Analizi

Rüzgar Hızı Değerlendirmesinde İki Parametreli Weibull Dağılımı İçin Üç Parametre Tahmin Metodunun Karşılaştırmalı Performans Analizi

Performance Comparison of New Generation Parameter Estimation Methods for Weibull Distribution to Compute Wind Energy Density

Influence of the ENSO and Monsoonal Season on Long-Term Wind Energy Potential in Malaysia

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