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Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

Author

Listed:
  • Alma Y. Alanis

    (University Center of Exact Sciences and Engineering, University of Guadalajara, Marcelino Garcia Barragan 1421, Guadalajara, Jalisco 44430, Mexico)

  • Oscar D. Sanchez

    (University Center of Exact Sciences and Engineering, University of Guadalajara, Marcelino Garcia Barragan 1421, Guadalajara, Jalisco 44430, Mexico)

  • Jesus G. Alvarez

    (University Center of Exact Sciences and Engineering, University of Guadalajara, Marcelino Garcia Barragan 1421, Guadalajara, Jalisco 44430, Mexico)

Abstract

Wind energy is one of the most promising alternatives as energy sources; however, to obtain the best results, producers need to forecast the wind speed, generated power and energy price in order to provide the appropriate tools for optimal operation, planning, control and marketing both for isolated wind systems and for those that are interconnected to a main distribution network. For the present work, a novel methodology is proposed for the forecasting of time series in wind energy systems; it consists of a high-order neural network that is trained on-line by the extended Kalman filter algorithm. Unlike most modern artificial intelligence methods of forecasting, which are based on hybridizations, data pre-filtering or deep learning methods, the proposed method is based on the simplicity of implementation, low computational complexity and real-time operation to produce 15-step-ahead forecasting in a time series of wind speed, generated power and energy price. The proposed scheme has been evaluated using real data from open access repositories of wind farms. The results show that an on-line training of the neural network produces high precision, without the need for any other information beyond a few past observations.

Suggested Citation

  • Alma Y. Alanis & Oscar D. Sanchez & Jesus G. Alvarez, 2021. "Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1075-:d:551971
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    References listed on IDEAS

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    Cited by:

    1. Bilal, Boudy & Adjallah, Kondo Hloindo & Sava, Alexandre & Yetilmezsoy, Kaan & Kıyan, Emel, 2022. "Wind power conversion system model identification using adaptive neuro-fuzzy inference systems: A case study," Energy, Elsevier, vol. 239(PB).
    2. Mumin Zhang & Yuzhi Wang & Haochen Zhang & Zhiyun Peng & Junjie Tang, 2023. "A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster," Mathematics, MDPI, vol. 11(3), pages 1-17, January.
    3. Eren Bas & Erol Eğrioğlu, 2023. "A new recurrent pi‐sigma artificial neural network inspired by exponential smoothing feedback mechanism," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 802-812, July.

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