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Investigations on the Eigen‐coordinates method for the 2‐parameter weibull distribution of wind speed

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  • Touré, Siaka

Abstract

The 2-parameter Weibull distribution is the hypothesis that is widely used in the fitting studies of random series of wind speeds. Several procedures are used to find the set of the two fitting parameters k and c. From an experimental study, the fitting parameters were first determined by the regression method. The basic ideas of the Eigen-coordinates method were reported by previous works, in the case of the 4-parameter Stauffer distribution. In the present paper, the new method is applied to identify the 2-parameter Weibull distribution. The differential equation was identified. Then the study disclosed a linear relationship with two Eigen-coordinates. Two complemental errors ɛj and ej were introduced, as criteria to assess the goodness-of-fit of the distribution. ɛj was linked to the linear relationship. ej was used to test the goodness-of-fit between the observed and Weibull cumulative distribution functions. Then the fitting parameters were determined using the Eigen-coordinates method. The results showed a better reliability.

Suggested Citation

  • Touré, Siaka, 2005. "Investigations on the Eigen‐coordinates method for the 2‐parameter weibull distribution of wind speed," Renewable Energy, Elsevier, vol. 30(4), pages 511-521.
    • Handle: RePEc:eee:renene:v:30:y:2005:i:4:p:511-521
    DOI: 10.1016/j.renene.2004.07.007
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    1. Al-Hasan, Mohammed & Nigmatullin, Raoul R., 2003. "Identification of the generalized Weibull distribution in wind speed data by the Eigen-coordinates method," Renewable Energy, Elsevier, vol. 28(1), pages 93-110.
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    4. Sulaiman, M.Yusof & Akaak, Ahmed Mohammed & Wahab, Mahdi Abd & Zakaria, Azmi & Sulaiman, Z.Abidin & Suradi, Jamil, 2002. "Wind characteristics of Oman," Energy, Elsevier, vol. 27(1), pages 35-46.
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    1. Safari, Bonfils & Gasore, Jimmy, 2010. "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda," Renewable Energy, Elsevier, vol. 35(12), pages 2874-2880.
    2. Christopher Jung, 2016. "High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series," Energies, MDPI, vol. 9(5), pages 1-20, May.
    3. Fawad, Muhammad & Yan, Ting & Chen, Lu & Huang, Kangdi & Singh, Vijay P., 2019. "Multiparameter probability distributions for at-site frequency analysis of annual maximum wind speed with L-Moments for parameter estimation," Energy, Elsevier, vol. 181(C), pages 724-737.
    4. Goh, H.H. & Lee, S.W. & Chua, Q.S. & Goh, K.C. & Teo, K.T.K., 2016. "Wind energy assessment considering wind speed correlation in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1389-1400.
    5. Saleh, H. & Abou El-Azm Aly, A. & Abdel-Hady, S., 2012. "Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt," Energy, Elsevier, vol. 44(1), pages 710-719.

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