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Artificial Intelligence in Wind Speed Forecasting: A Review

Author

Listed:
  • 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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    Cited by:

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    3. Monica Borunda & Adrián Ramírez & Raul Garduno & Carlos García-Beltrán & Rito Mijarez, 2023. "Enhancing Long-Term Wind Power Forecasting by Using an Intelligent Statistical Treatment for Wind Resource Data," Energies, MDPI, vol. 16(23), pages 1-34, December.
    4. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    5. Vladimir Simankov & Pavel Buchatskiy & Semen Teploukhov & Stefan Onishchenko & Anatoliy Kazak & Petr Chetyrbok, 2023. "Review of Estimating and Predicting Models of the Wind Energy Amount," Energies, MDPI, vol. 16(16), pages 1-24, August.
    6. Latifa A. Yousef & Hibba Yousef & Lisandra Rocha-Meneses, 2023. "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions," Energies, MDPI, vol. 16(24), pages 1-27, December.
    7. Muyuan Du & Zhimeng Zhang & Chunning Ji, 2025. "Prediction for Coastal Wind Speed Based on Improved Variational Mode Decomposition and Recurrent Neural Network," Energies, MDPI, vol. 18(3), pages 1-28, January.
    8. Carlos Cacciuttolo & Martin Navarrete & Edison Atencio, 2024. "Renewable Wind Energy Implementation in South America: A Comprehensive Review and Sustainable Prospects," Sustainability, MDPI, vol. 16(14), pages 1-45, July.
    9. 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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