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Artificial Intelligence in Wind Speed Forecasting: A Review

Author

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  • Sandra Minerva Valdivia-Bautista

    (Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara 44430, Mexico)

  • José Antonio Domínguez-Navarro

    (Department of Electrical Engineering, School of Engineering and Architecture, University of Zaragoza, C/María de Luna, 50018 Zaragoza, Spain)

  • Marco Pérez-Cisneros

    (Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Guadalajara 44430, Mexico)

  • Carlos Jesahel Vega-Gómez

    (Centro Universitario de CuTlajomulco, Universidad de Guadalajara, Tlajomulco de Zuñiga 45670, Mexico)

  • Beatriz Castillo-Téllez

    (Centro Universitario de CuTonalá, Universidad de Guadalajara, Tonalá 45425, Mexico)

Abstract

Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric. The research results indicate that artificial neural network (ANN)-based models can provide accurate wind forecasting and essential information about the specific location of potential wind use for a power plant by understanding the future wind speed values.

Suggested Citation

  • Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2457-:d:1087768
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    References listed on IDEAS

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    Cited by:

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    5. Navid Shirzadi & Fuzhan Nasiri & Ramanunni Parakkal Menon & Pilar Monsalvete & Anton Kaifel & Ursula Eicker, 2023. "Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction," Energies, MDPI, vol. 16(17), pages 1-17, August.

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