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International Conference on Applied Technologies

ICAT 2019: Applied Technologies pp 348–362Cite as

Wind Energy Forecasting with Artificial Intelligence Techniques: A Review

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1194))

Abstract

The World Wind Energy Association (WWEA) forecasts that installed wind capacity worldwide will reach 800 GW by the end of 2021. Because wind is a random resource, both in speed and direction, the short-term forecasting of wind energy has become an important issue to be investigated. In this paper, a Systematic Literature Review (SLR) on non-parametric models and techniques for predicting short-term wind energy is presented based on four research questions related to both already applied methodologies and wind physical variables in order to determine the state of the art for the development of the research project “Artificial intelligence system for the short-term prediction of the energy production of the Villonaco wind farm”. The results indicate that artificial neural networks (ANN) and support-vector machines (SVMs) were mainly used in related studies. In addition, ANNs are highlighted in comparison with other techniques of Wind Energy Forecasting.

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Acknowledgement

The authors acknowledge the support of the ‘Universidad Nacional de Loja’ by means of the research project: Artificial intelligence system for the short-term prediction of the energy production of the Villonaco wind farm. 26-DI-FEIRNNR-2019.

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Authors and Affiliations

  1. Universidad Nacional de Loja, Facultad de Energía, Ciudad Universitaria Guillermo Falconí, Loja, Ecuador

    Jorge Maldonado-Correa, Marcelo Valdiviezo, Juan Solano, Marco Rojas & Carlos Samaniego-Ojeda

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  1. Jorge Maldonado-Correa
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  2. Marcelo Valdiviezo
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  3. Juan Solano
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  4. Marco Rojas
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  5. Carlos Samaniego-Ojeda
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Corresponding author

Correspondence to Jorge Maldonado-Correa .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica del Norte, Ibarra, Ecuador

    Marcelo Zambrano Vizuete

  3. Universidad Técnica Particular de Loja, Loja, Ecuador

    Pablo Torres-Carrión

  4. Universidad de las Fuerzas Armadas (ESPE), Quito, Ecuador

    Sergio Montes León

  5. Universidad Politécnica Salesiana, Guayaquil, Ecuador

    Guillermo Pizarro Vásquez

  6. International University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Benjamin Durakovic

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Maldonado-Correa, J., Valdiviezo, M., Solano, J., Rojas, M., Samaniego-Ojeda, C. (2020). Wind Energy Forecasting with Artificial Intelligence Techniques: A Review. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-42520-3_28

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