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

Article Metrics: (Click on the Metric tab below to see the detail)

Article Info
Published: 10-07-2018
Section: Original Research Article
In Vol 7, No 2 (2018): July 2018
Fulltext PDF Tell your colleagues Email the author

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
  1. Ahmed, S.A., (2013). Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq. International Journal of Physical Sciences, 8(5), 186–192..
  2. Al-Mulali, U., & Sab, C. N. B. C., (2012). The impact of energy consumption and CO2 emission on the economic growth and financial development in the Sub Saharan African countries. Energy,, 180–186.
  3. Bugaje I. M., (2006). Renewable energy for sustainable development in Africa: a review,. Renew. Sustain. Energy Rev.,603–612.
  4. Celik, A.N., (2004). A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy, 29(4), 593–604.
  5. Dahmouni, A. W., Salah, M. B., Askri, F., Kerkeni, C., & Nasrallah, S. B., (2011). Assessment of wind energy potential and optimal electricity generation in Borj-Cedria , Tunisia. Renewable and Sustainable Energy Reviews, 15(1), 815–820.
  6. Dinler, A. & Akdag, S.A., (2009). A new method to estimate Weibull parameters for wind energy applications. 50, 1761–1766.
  7. Garcia, A., Torres, J. L., Prieto, E., & De Francisco, A., (1998). Fitting wind speed distributions: a case study. Solar Energy, 62(2), 139–144..
  8. Kasra, M., Omid,A., Ali, M. & Navid, G.M.J., (2016). Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Conversion and Management, 108(November), 322–335.
  9. Kidmo, D. K., Danwe, R., Doka, S. Y., & Djongyang, N., (2015). Statistical analysis of wind speed distribution based on six Weibull Methods for wind power evaluation in Garoua, Cameroon.,18,105–125..
  10. Kimatu, W., Ayenagbo, J. N., Rongcheng, K., (2011). A model for a sustainable energy supply strategy for the social-economic development of Togo.
  11. Legates, D. R., & McCabe, G. J., (1999). Goodness-of-fit measures in hydrologic and hydro climatic model validation,. Water Resour. Res.,, 35, 233–241.
  12. Mostafaeipour, A., Sedaghat, A., Dehghan-Niri, A. A., & Kalantar, V., (2011). Author ’ s personal copy Wind energy feasibility study for city of Shahrbabak in Iran. Renewable and Sustainable Energy Reviews, 15, 2545–2556.
  13. Rocha, P. A. C., de Sousa, R. C., de Andrade, C. F., & da Silva, M. E. V., (2012). Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Applied Energy, 89(1), 395–400.
  14. Sahin, A.D., (2004). Progress and recent trends in wind energy. Progress in Energy and Combustion Science, 30(5), 501–543..
  15. Salami, A.A., Ajavon, A. S. A, Kodjo, M.K. & Bedja, K., (2013). Contribution to improving the modeling of wind and evaluation of the wind potential of the site of Lome: Problems of taking into account the frequency of calm winds. Renewable Energy, 50, 449–455. A
  16. Salami, A.A., Ajavon, A. S. A, Kodjo, M.K. & Bedja, K., (2016). Evaluation of Wind Potential for an Optimum Choice of Wind Turbine Generator on the Sites of Lome, Accra, and Cotonou Located in the Gulf of Guinea.,. Int. Journal of Renewable Energy Development,, 5(3), 211–223. Seguro, J. V & Lambert, T.W., (2000). Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics, 85(1), 75–84.
  17. Yuan F. Q. Barabadi A., L.J.M.G.A.H.S., (2015). Performance evaluation for maximum likelihood and moment parameter estimation methods on classical two Weibull distributions. Ind. Eng. Eng. Manag. (IEEM), 2015 IEEE Int. Conf.,, 2015, 802–806.