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Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea

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  • Zhang, Hua
  • Yu, Yong-Jing
  • Liu, Zhi-Yuan

Abstract

This paper is to study Weibull distribution and Maximum Entropy Principle applied to fit the wind speed frequency distribution of the intertidal zone anemometer towers. Comparisons of the results from Weibull distribution and Maximum Entropy distribution are made for the characteristics of the wind speed distribution and the variation with height in the offshore area. The Maximum Entropy distribution is found to perform adequately and accurately in fitting the wind speed frequency distribution with height. It is shown that the frequency peak value of Weibull distribution is lower than the measured maximum frequency at the same height, and the fitting accuracy of the Maximum Entropy Principle is significantly higher than that of Weibull distribution. Furthermore, the mean errors of average effective wind power density calculated from the five-parameter Maximum Entropy distribution as well as from Weibull distribution are 1.71Wm−2 and 7.48Wm−2 respectively. Except for these findings, limitations and problems existing in the procedure of fitting the annual wind speed probability distribution are also discussed.

Suggested Citation

  • Zhang, Hua & Yu, Yong-Jing & Liu, Zhi-Yuan, 2014. "Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea," Applied Energy, Elsevier, vol. 114(C), pages 931-938.
    • Handle: RePEc:eee:appene:v:114:y:2014:i:c:p:931-938
    DOI: 10.1016/j.apenergy.2013.07.040
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    1. Li, Meishen & Li, Xianguo, 2005. "MEP-type distribution function: a better alternative to Weibull function for wind speed distributions," Renewable Energy, Elsevier, vol. 30(8), pages 1221-1240.
    2. Carapellucci, Roberto & Giordano, Lorena, 2013. "A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data," Applied Energy, Elsevier, vol. 101(C), pages 541-550.
    3. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    4. Chang, Tian-Pau & Ko, Hong-Hsi & Liu, Feng-Jiao & Chen, Pai-Hsun & Chang, Ying-Pin & Liang, Ying-Hsin & Jang, Horng-Yuan & Lin, Tsung-Chi & Chen, Yi-Hwa, 2012. "Fractal dimension of wind speed time series," Applied Energy, Elsevier, vol. 93(C), pages 742-749.
    5. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    6. Akdag, S.A. & Bagiorgas, H.S. & Mihalakakou, G., 2010. "Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean," Applied Energy, Elsevier, vol. 87(8), pages 2566-2573, August.
    7. Calif, Rudy, 2012. "PDF models and synthetic model for the wind speed fluctuations based on the resolution of Langevin equation," Applied Energy, Elsevier, vol. 99(C), pages 173-182.
    8. Chang, Tian Pau, 2011. "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application," Applied Energy, Elsevier, vol. 88(1), pages 272-282, January.
    9. Celik, Ali N. & Kolhe, Mohan, 2013. "Generalized feed-forward based method for wind energy prediction," Applied Energy, Elsevier, vol. 101(C), pages 582-588.
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