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

Author

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  • 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

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    Cited by:

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    2. Catra Indra Cahyadi & Suwarno Suwarno & Aminah Asmara Dewi & Musri Kona & Muhammad Arif & Muhammad Caesar Akbar, 2023. "Solar Prediction Strategy for Managing Virtual Power Stations," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 503-512, July.

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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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