The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

DOI: https://doi.org/10.14710/ijred.7.2.139-150

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Article Info
Published: 10-07-2018
Section: Original Research Article
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In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.

Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available online

How to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.

https://doi.org/10.14710/ijred.7.2.139-150

Keywords

Odd bin wind speed time series, Even bin wind speed time series, Weibull parameters, Statistical analysis, Comparative evaluation.

  1. Adekunlé Akim Salami 
    Equipe de Recherche en Sciences de l’Ingénieur (ERSI), Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs (ENSI), University of Lomé, BP 1515, Lomé TOGO, Togo
  2. Ayité Sénah Akoda Ajavon 
    Equipe de Recherche en Sciences de l’Ingénieur (ERSI), Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs (ENSI), University of Lomé, BP 1515, Lomé TOGO, Togo
  3. Mawugno Koffi Kodjo 
    Equipe de Recherche en Sciences de l’Ingénieur (ERSI), Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs (ENSI), University of Lomé, BP 1515, Lomé TOGO, Togo
  4. Seydou Ouedraogo 
    Polytechnic University of bobo-Dioulasso, Burkina-Faso, Burkina Faso
  5. Koffi-Sa Bédja 
    Equipe de Recherche en Sciences de l’Ingénieur (ERSI), Department of Electrical Engineering, Ecole Nationale Supérieure d’Ingénieurs (ENSI), University of Lomé, BP 1515, Lomé TOGO, Togo
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