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An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems

Author

Listed:
  • Saira Al-Zadjali

    (Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman)

  • Ahmed Al Maashri

    (Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman)

  • Amer Al-Hinai

    (Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman
    Sustainable Energy Research Center, Sultan Qaboos University, Al Khodh 123, Oman)

  • Sultan Al-Yahyai

    (Information and Technology, Mazoon Electricity Company, Fanja 600, Oman)

  • Mostafa Bakhtvar

    (Electrical & Computer Engineering, Sultan Qaboos University, Al Khodh 123, Oman)

Abstract

This paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model provides wind speed nowcasting using a combination of perturbed observation ensemble networks and artificial neural networks. The model was validated and evaluated via simulation using data that were measured from wind masts. The simulation results show that the proposed model improved the normalized root mean square error by 20.9% compared to other contending approaches. In terms of prediction interval coverage probability, our proposed model shows a 17.8% improvement, all while using a smaller number of neural networks. Furthermore, the proposed model has an execution time that is one order of magnitude faster than other contenders.

Suggested Citation

  • Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Sultan Al-Yahyai & Mostafa Bakhtvar, 2019. "An Accurate, Light-Weight Wind Speed Predictor for Renewable Energy Management Systems," Energies, MDPI, vol. 12(22), pages 1-20, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4355-:d:287275
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    References listed on IDEAS

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    1. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. Quan, Hao & Srinivasan, Dipti & Khosravi, Abbas, 2014. "Uncertainty handling using neural network-based prediction intervals for electrical load forecasting," Energy, Elsevier, vol. 73(C), pages 916-925.
    4. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2012. "Unrestricted wind farm layout optimization (UWFLO): Investigating key factors influencing the maximum power generation," Renewable Energy, Elsevier, vol. 38(1), pages 16-30.
    5. Al-Yahyai, Sultan & Charabi, Yassine & Al-Badi, Abdullah & Gastli, Adel, 2012. "Nested ensemble NWP approach for wind energy assessment," Renewable Energy, Elsevier, vol. 37(1), pages 150-160.
    6. G P Zhang & V L Berardi, 2001. "Time series forecasting with neural network ensembles: an application for exchange rate prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(6), pages 652-664, June.
    7. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    8. Cadenas, Erasmo & Rivera, Wilfrido, 2007. "Wind speed forecasting in the South Coast of Oaxaca, México," Renewable Energy, Elsevier, vol. 32(12), pages 2116-2128.
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    Cited by:

    1. Evangelos Spiliotis & Fotios Petropoulos & Konstantinos Nikolopoulos, 2020. "The Impact of Imperfect Weather Forecasts on Wind Power Forecasting Performance: Evidence from Two Wind Farms in Greece," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Saira Al-Zadjali & Ahmed Al Maashri & Amer Al-Hinai & Rashid Al Abri & Swaroop Gajare & Sultan Al Yahyai & Mostafa Bakhtvar, 2021. "A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems," Energies, MDPI, vol. 14(23), pages 1-20, November.
    3. Mostafa Bakhtvar & Amer Al-Hinai, 2021. "Robust Operation of Hybrid Solar–Wind Power Plant with Battery Energy Storage System," Energies, MDPI, vol. 14(13), pages 1-18, June.

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