An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: A new approach applied to the northeast region of Brazil

Andrade, Carla Freitas de; Neto, Hely Falcão Maia; Rocha, Paulo Alexandre Costa; Silva, Maria Eugênia Vieira da

doi:10.1016/j.enconman.2014.06.046

Cited by 91 publications

(52 citation statements)

References 27 publications

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“…Rainfall being set as an exogenous variable in the making of the ARIMAX models was motivated by the influence that it exerts on the intensity of wind speed in the northeastern region of Brazil, as commented in [23][24][25]. In order to better identify this influence from the data used in this paper, Figure 3 shows the relationships between the monthly mean wind speed (black line) and precipitation (blue line) of the study regions Fortaleza (Figure 3a), Parnaíba (Figure 3b), and Natal (Figure 3c).…”

Section: Resultsmentioning

confidence: 99%

“…Due to the importance of the variables of pressure and temperature in relation to the displacement of air masses, it was decided to relate both of these with the wind speed in applying the proposed model. The motivation to choose precipitation is because, in the literature, there are several studies that show differences in the intensity of the winds over the northeastern region of Brazil during the rainy and dry periods [23][24][25].…”

Section: Box-jenkins and Box-tiao Modelingmentioning

confidence: 99%

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Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

et al. 2018

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This work proposes hybrid models combining time-series models (using linear functions) and artificial intelligence (using a nonlinear function) that can be used to provide monthly mean wind speed predictions for the Brazilian northeast region. These might be useful for wind power generation; for example, they could acquire important information on how the local wind potential can be usable for a possible wind power plant through understanding future wind speed values. To create the proposed hybrid models, it was necessary to set the wind speed variable as a dependent variable of exogenous variables (i.e., pressure, temperature, and precipitation). Thus, it was possible to consider the meteorological characteristics of the study regions. It is possible to verify the hybrid models' efficiency in providing perfect adjustments to the observed data. This statement is based on the low values found in the error statistical analysis, i.e., an error of approximately 5.0% and a Nash-Sutcliffe coefficient near to 0.96. These results were certainly important in predicting the wind speed time-series, which was similar to the observed wind speed time-series profile. Great similarities of maximums and minimums between the series were evident and showed the capacity of the models to represent the seasonality characteristics.

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Section: Resultsmentioning

confidence: 99%

Section: Box-jenkins and Box-tiao Modelingmentioning

confidence: 99%

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

et al. 2018

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“…Andrade et al . performed a comparative study among several Weibull methods to compute the Weibull parameters for prediction of the wind resource in the northeast region of Brazil. Akdağ and Dinler proposed a method named as power density (PD) to compute the Weibull parameters.…”

Section: Introductionmentioning

confidence: 99%

A combined method to estimate wind speed distribution based on integrating the support vector machine with firefly algorithm

Gani

Mohammadi

Shamshirband

et al. 2015

Env Prog and Sustain Energy

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A new hybrid approach by integrating the support vector machine (SVM) with firefly algorithm (FFA) is proposed to estimate shape (k) and scale (c) parameters of the Weibull distribution function according to previously established analytical methods. The extracted data of two widely successful methods utilized to compute parameters k and c were used as learning and testing information for the SVM-FFA method. The simulations were performed on both daily and monthly scales to draw further conclusions. The performance of SVM-FFA method was compared against other existing techniques to demonstrate its efficiency and viability. The results conclusively indicate that SVM-FFA method provides further precision in the predictions. Nevertheless, for daily estimations, the applicability of proposed method could not be feasible owing to high day-by-day fluctuations of parameters k, whereas the results of monthly estimation are completely appealing and precise. In summary, the SVM-FFA is a highly viable and efficient technique to estimate wind speed distribution on monthly scale. It is expected that the proposed method would be profitable for wind researchers and experts to be used in many practical applications, such as evaluating the wind energy potential and making a proper decision to nominate the optimal wind turbines. V C 2015 American Institute of Chemical Engineers Environ Prog, 35: 867-875, 2016

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“…The PDF values obtained from the time-series wind data should be compared with the PDF values obtained by using each method. The RMSE is defined [5,18,38]:…”

Section: Rmsementioning

confidence: 99%

“…The suitability of the estimation was assessed by the R 2 test and the most appropriate method is the one with an R 2 value closest to 1 [37,38,40].…”

Section: Analysis Of Variance (R 2 )mentioning

confidence: 99%

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

Kang

Huh

2018

Energies

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On Jeju Island, South Korea, an investigation was conducted to determine the best method for estimating Weibull parameters. Six methods commonly used in many fields of the wind energy industry were reviewed: the empirical, moment, graphical, energy pattern factor, maximum likelihood, and modified maximum likelihood methods. In order to improve the reliability of a research result, five-year actual wind speed data taken from nine sites with various topographical conditions were used for the estimation. Furthermore, the effect of various topographical conditions on the accuracy of the methods was analyzed and 10 bin interval types were applied to determine the most appropriate bin interval based on their performances. Weibull distributions that were estimated using these methods were compared with the observed wind speed distribution. Then the accuracy of each method was evaluated using four accuracy tests. The results showed that of the six methods, the moment method had the best performance regardless of topographical conditions, while the graphical method performed the worst. Additionally, topographical conditions did not affect the accuracy ranking of the methods for estimating the Weibull parameters, while an increase of terrain complexity resulted in an increase of discrepancy between the estimated Weibull distribution and the frequency of the observed wind speed data. In addition, the choice in bin interval greatly affected the accuracy of the graphical method while it did not depend on the accuracy of the modified maximum likelihood method.

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An efficiency comparison of numerical methods for determining Weibull parameters for wind energy applications: A new approach applied to the northeast region of Brazil

Cited by 91 publications

References 27 publications

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

Innovative Hybrid Modeling of Wind Speed Prediction Involving Time-Series Models and Artificial Neural Networks

A combined method to estimate wind speed distribution based on integrating the support vector machine with firefly algorithm

Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea

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