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Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan

Author

Listed:
  • Ayman Al-Quraan

    (Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

  • Bashar Al-Mhairat

    (Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

  • Ahmad M. A. Malkawi

    (Mechatronics Engineering Department, The University of Jordan, Amman 11942, Jordan)

  • Ashraf Radaideh

    (Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

  • Hussein M. K. Al-Masri

    (Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

Abstract

The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination ( R 2 ), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density ( E D ) and the total wind energy ( E T ) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m 2 to nearly 1250 kWh/m 2 . In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern.

Suggested Citation

  • Ayman Al-Quraan & Bashar Al-Mhairat & Ahmad M. A. Malkawi & Ashraf Radaideh & Hussein M. K. Al-Masri, 2023. "Optimal Prediction of Wind Energy Resources Based on WOA—A Case Study in Jordan," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:3927-:d:1075857
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    References listed on IDEAS

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    3. Ban, Guihua & Chen, Yan & Xiong, Zhenhua & Zhuo, Yixin & Huang, Kui, 2024. "The univariate model for long-term wind speed forecasting based on wavelet soft threshold denoising and improved Autoformer," Energy, Elsevier, vol. 290(C).
    4. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).

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