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A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems

Author

Listed:
  • Saira Al-Zadjali

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

  • Ahmed Al Maashri

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

  • Amer Al-Hinai

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

  • Rashid Al Abri

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

  • Swaroop Gajare

    (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

    (Sustainable Energy Research Center, Sultan Qaboos University, Al Khodh 123, Oman)

Abstract

To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and interpolation. To check the efficacy of the proposed model, the power output from a real wind farm is predicted and compared with the actual recorded measurements. The results of the comprehensive analysis show that the proposed model outperforms contending models in terms of accuracy and execution time. Therefore, this model can be used by operators to reliably generate a dispatch plan.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7878-:d:686857
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    References listed on IDEAS

    as
    1. 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.
    2. Took, C. Cheong & Strbac, G. & Aihara, K. & Mandic, D.P., 2011. "Quaternion-valued short-term joint forecasting of three-dimensional wind and atmospheric parameters," Renewable Energy, Elsevier, vol. 36(6), pages 1754-1760.
    3. Gallego, C. & Pinson, P. & Madsen, H. & Costa, A. & Cuerva, A., 2011. "Influence of local wind speed and direction on wind power dynamics – Application to offshore very short-term forecasting," Applied Energy, Elsevier, vol. 88(11), pages 4087-4096.
    4. Velázquez, Sergio & Carta, José A. & Matías, J.M., 2011. "Influence of the input layer signals of ANNs on wind power estimation for a target site: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(3), pages 1556-1566, April.
    5. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    6. Zhang, Jie & Cui, Mingjian & Hodge, Bri-Mathias & Florita, Anthony & Freedman, Jeffrey, 2017. "Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales," Energy, Elsevier, vol. 122(C), pages 528-541.
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    Cited by:

    1. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.

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