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A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution

Author

Listed:
  • Hanifa Teimourian

    (Department of Electrical Engineering, Faculty of Engineering, University of Near East, Northern Cyprus, Via Mersin 10, Lefkosa 99138, Turkey)

  • Mahmoud Abubakar

    (Department of Aeronautical Engineering, Faculty of Aviation and Space Sciences, University of Kyrenia, Northern Cyprus, Via Mersin 10, Girne 99320, Turkey)

  • Melih Yildiz

    (Department of Aeronautical Engineering, Faculty of Aviation and Space Sciences, Erciyes University, Kayseri 38280, Turkey)

  • Amir Teimourian

    (Department of Aeronautical Engineering, Faculty of Aviation and Space Sciences, University of Kyrenia, Northern Cyprus, Via Mersin 10, Girne 99320, Turkey)

Abstract

Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis and wind energy potential assessments for a given location. Such assessments require the implementation of an accurate and suitable wind distribution model. Therefore, in the quest for a well-fitted model, eight methods for estimating the Weibull parameters are investigated in this paper. The methods were then investigated by employing statistical tools, and their performances have been discussed in terms of various error indicators such as root mean squared error (RMSE), regression error (R2), chi-square (X2), and mean absolute error (MAE). Meteorological data for diverse terrain from 14 provinces with 30 sites scattered across Iran were employed to examine the performance of the investigated methods. The results demonstrated that the empirical method has superiority over the investigated technique in terms of errors.

Suggested Citation

  • Hanifa Teimourian & Mahmoud Abubakar & Melih Yildiz & Amir Teimourian, 2022. "A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution," Energies, MDPI, vol. 15(15), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5684-:d:880900
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    References listed on IDEAS

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    1. Ucar, Aynur & Balo, Figen, 2010. "Assessment of wind power potential for turbine installation in coastal areas of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1901-1912, September.
    2. Camilo Carrillo & José Cidrás & Eloy Díaz-Dorado & Andrés Felipe Obando-Montaño, 2014. "An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain)," Energies, MDPI, vol. 7(4), pages 1-25, April.
    3. Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
    4. Ouammi, Ahmed & Dagdougui, Hanane & Sacile, Roberto & Mimet, Abdelaziz, 2010. "Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1959-1968, September.
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