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Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF

Author

Listed:
  • Hiba H. Darwish

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

  • Ayman Al-Quraan

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

Abstract

Estimating wind energy at a specific wind site depends on how well the real wind data in that area can be represented using an appropriate distribution function. In fact, wind sites differ in the extent to which their wind data can be represented from one region to another, despite the widespread use of the Weibull function in representing the wind speed in various wind locations in the world. In this study, a new probability distribution model (normal PDF) was tested to implement wind speed at several wind locations in Jordan. The results show high compatibility between this model and the wind resources in Jordan. Therefore, this model was used to estimate the values of the wind energy and the extracted energy of wind turbines compared to those obtained by the Weibull PDF. Several artificial intelligence techniques were used (GA, BFOA, SA, and a neuro-fuzzy method) to estimate and predict the parameters of both the normal and Weibull PDFs that were reflected in conjunction with the actual observed data of wind probabilities. Afterward, the goodness of fit was decided with the aid of two performance indicators (RMSE and MAE). Surprisingly, in this study, the normal probability distribution function (PDF) outstripped the Weibull PDF, and interestingly, BFOA and SA were the most accurate methods. In the last stage, machine learning was used to classify and predict the error level between the actual probability and the estimated probability based on the trained and tested data of the PDF parameters. The proposed novel methodology aims to predict the most accurate parameters, as the subsequent energy calculation phases of wind depend on the proper selection of these parameters. Hence, 24 classifier algorithms were used in this study. The medium tree classifier shows the best performance from the accuracy and training time points of view, while the ensemble-boosted trees classifier shows poor performance regarding providing correct predictions.

Suggested Citation

  • Hiba H. Darwish & Ayman Al-Quraan, 2023. "Machine Learning Classification and Prediction of Wind Estimation Using Artificial Intelligence Techniques and Normal PDF," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3270-:d:1064595
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    References listed on IDEAS

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    Cited by:

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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).
    5. Wang, Longze & Zhang, Yan & Li, Zhehan & Huang, Qiyu & Xiao, Yuxin & Yi, Xinxing & Ma, Yiyi & Li, Meicheng, 2023. "P2P trading mode for real-time coupled electricity and carbon markets based on a new indicator green energy," Energy, Elsevier, vol. 285(C).

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