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Evaluation and Analysis of Wind Speed with the Weibull and Rayleigh Distribution Models for Energy Potential Using Three Models

Author

Listed:
  • Muhammad Fitra Zambak

    (Department of Electrical Engineering, Universitas Muhammadiyah Sumatera Utara, Indonesia)

  • Catra Indra Cahyadi

    (Department of Electrical Engineering, Universitas Muhammadiyah Sumatera Utara, Indonesia; Politeknik Penerbangan Medan, Indonesia)

  • Jufri Helmi

    (Department of Electrical Engineering, Universitas Muhammadiyah Sumatera Utara, Indonesia)

  • Tengku Machdhalie Sofie

    (Department of Electrical Engineering, Universitas Muhammadiyah Sumatera Utara, Indonesia)

  • Suwarno Suwarno

    (Department of Electrical Engineering, Universitas Muhammadiyah Sumatera Utara, Indonesia)

Abstract

Medan has a tropical climate and has the potential to support additional renewable energy, one of which is wind energy. Analysis of wind speed in Medan in particular has not been conducted to determine the potential for renewable energy. Research on wind speed in Medan, which ranges from 3.5m/s to 7.5m/s, has been carried out, but its potential has not been analyzed and evaluated. This study was conducted to analyze the shape factor and scale for wind speed using the Weibull and Rayleigh distribution, and three evaluation models were proposed, namely the correlation coefficient (R2), Chi-Square (?2), and Root mean square error (RMSE). Wind speed data that is used to analyze and evaluate obtained from the Meteorology, Climatology, and Geophysics Agency for a period of three years, 2017 to 2019 in Medan. The probability density distribution function (Pdf) is described based on the shape (k) and scale (c) parameters obtained from the above data analysis. These two parameters are very important to be observed related to the potential of electrical energy produced in a place or area. The analysis result shows that Weibull is better than Rayleigh distribution based on Pdf. Meanwhile statistical analysis, Weibull distribution is better than Rayleigh distribution based on R2. But on the other hand, the Rayleigh distribution is better than the Weibull distribution based on Chi-Square and RMSE. In addition to the analysis and evaluation, the potential for wind energy to be obtained is around 79.5 Watt/m2.

Suggested Citation

  • Muhammad Fitra Zambak & Catra Indra Cahyadi & Jufri Helmi & Tengku Machdhalie Sofie & Suwarno Suwarno, 2023. "Evaluation and Analysis of Wind Speed with the Weibull and Rayleigh Distribution Models for Energy Potential Using Three Models," International Journal of Energy Economics and Policy, Econjournals, vol. 13(2), pages 427-432, March.
  • Handle: RePEc:eco:journ2:2023-02-48
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    References listed on IDEAS

    as
    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. Ahmed Shata, A.S. & Hanitsch, R., 2006. "Evaluation of wind energy potential and electricity generation on the coast of Mediterranean Sea in Egypt," Renewable Energy, Elsevier, vol. 31(8), pages 1183-1202.
    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. Gonçalves, Helena Martins & Lourenço, Tiago Ferreira & Silva, Graça Miranda, 2016. "Green buying behavior and the theory of consumption values: A fuzzy-set approach," Journal of Business Research, Elsevier, vol. 69(4), pages 1484-1491.
    5. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    6. Ramayah, T. & Lee, Jason Wai Chow & Mohamad, Osman, 2010. "Green product purchase intention: Some insights from a developing country," Resources, Conservation & Recycling, Elsevier, vol. 54(12), pages 1419-1427.
    7. Thi Thu Huong Nguyen & Zhi Yang & Ninh Nguyen & Lester W. Johnson & Tuan Khanh Cao, 2019. "Greenwash and Green Purchase Intention: The Mediating Role of Green Skepticism," Sustainability, MDPI, vol. 11(9), pages 1-16, May.
    8. Algifri, Abdulla H., 1998. "Wind energy potential in Aden-Yemen," Renewable Energy, Elsevier, vol. 13(2), pages 255-260.
    9. Weisser, D, 2003. "A wind energy analysis of Grenada: an estimation using the ‘Weibull’ density function," Renewable Energy, Elsevier, vol. 28(11), pages 1803-1812.
    10. Suwarno Suwarno & M. Fitra Zambak, 2021. "The Probability Density Function for Wind Speed Using Modified Weibull Distribution," International Journal of Energy Economics and Policy, Econjournals, vol. 11(6), pages 544-550.
    11. Jaramillo, O.A. & Borja, M.A., 2004. "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case," Renewable Energy, Elsevier, vol. 29(10), pages 1613-1630.
    12. Bivona, S. & Burlon, R. & Leone, C., 2003. "Hourly wind speed analysis in Sicily," Renewable Energy, Elsevier, vol. 28(9), pages 1371-1385.
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    Cited by:

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    More about this item

    Keywords

    Wind speed; Pdf; Weibull and Rayleigh distribution; wind energy potential; R2; ?2; and RMSE;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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