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A new hybrid model for point and probabilistic forecasting of wind power

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  • Tahmasebifar, Reza
  • Moghaddam, Mohsen Parsa
  • Sheikh-El-Eslami, Mohammad Kazem
  • Kheirollahi, Reza

Abstract

The accurate and reliable forecasting of wind power is of great importance for electrical systems’ control and operation. However, the intermittent nature of wind power generation implies a complicated forecasting framework. In this paper, a new hybrid model including three steps is proposed for point and probabilistic forecasting of wind power. Within the first step, by using data preprocessing methods, proposed weighted Extreme Learning Machine (ELM) by Mutual Information, and bootstrap approach, point forecasting and variance of the model uncertainties are estimated. In the second step, by employing ELM, bootstrap approach, and an ensemble structure, the noise variance is calculated. During the final step, to improve the results of the probabilistic forecasting, methods consisting of ELM, bootstrap, improved particle swarm optimization based on information feedback models and a new proposed prediction interval based objective function are used. Effectiveness of the proposed hybrid model is verified by employing real data of Australian wind farms for 1-h ahead and day ahead forecasting.

Suggested Citation

  • Tahmasebifar, Reza & Moghaddam, Mohsen Parsa & Sheikh-El-Eslami, Mohammad Kazem & Kheirollahi, Reza, 2020. "A new hybrid model for point and probabilistic forecasting of wind power," Energy, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:energy:v:211:y:2020:i:c:s036054422032123x
    DOI: 10.1016/j.energy.2020.119016
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    6. 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).
    7. Li, Yanting & Wu, Zhenyu & Su, Yan, 2023. "Adaptive short-term wind power forecasting with concept drifts," Renewable Energy, Elsevier, vol. 217(C).
    8. Fan, Huijing & Zhen, Zhao & Liu, Nian & Sun, Yiqian & Chang, Xiqiang & Li, Yu & Wang, Fei & Mi, Zengqiang, 2023. "Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method," Energy, Elsevier, vol. 266(C).

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