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Comparative Study of Regression Models Applied to the Prediction of Energy Generated by a Micro Wind Turbine

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 749))

Abstract

To create an efficient electricity system that includes renewable energy generation, able to respond correctly to the variability of power generation, it is essential to establish future production. For this reason, in this work, the performance of different regression models when predicting the energy produced by a small wind turbine based on meteorological variables is compared, seeking the best predictions. Four methods are evaluated: polynomial, bayesian, support vector machine, and artificial neural network. Several metrics are used to compare the models, such as Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Median Absolute Error and Coefficient of Determination, along with hypothesis testing.

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Acknowledgements

Míriam Timiraos’s research was supported by the “Xunta de Galicia” (Regional Government of Galicia) through grants to industrial PhD (http://gain.xunta.gal/), under the “Doutoramento Industrial 2022” grant with reference: 04_IN606D_2022_ 2692965.

Álvaro Michelena’s research was supported by the Spanish Ministry of Universities (https://www.universidades.gob.es/), under the “Formación de Profesorado Universitario” grant with reference: FPU21/00932.

CITIC, as a Research Center of the University System of Galicia, is funded by Consellería de Educación, Universidade e Formación Profesional of the Xunta de Galicia through the European Regional Development Fund (ERDF) and the Secretaría Xeral de Universidades (Ref. ED431G 2019/01).

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Authors and Affiliations

  1. University of A Coruña, CTC, CITIC, Department of Industrial Engineering, Calle Mendizabal s/n, 15471, Ferrol, A Coruña, Spain

    Antonio Díaz-Longueira, Míriam Timiraos, Álvaro Michelena, Francisco Zayas-Gato, José-Luis Casteleiro-Roca, Esteban Jove, Héctor Quintián & José Luis Calvo-Rolle

  2. University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovic̀a 6, 106314, Novi Sad, Serbia

    Dragan Simić

Authors
  1. Antonio Díaz-Longueira
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  2. Míriam Timiraos
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  3. Álvaro Michelena
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  4. Francisco Zayas-Gato
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  5. José-Luis Casteleiro-Roca
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  6. Esteban Jove
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  7. Héctor Quintián
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  8. Dragan Simić
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  9. José Luis Calvo-Rolle
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Corresponding author

Correspondence to Antonio Díaz-Longueira .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Díaz-Longueira, A. et al. (2023). Comparative Study of Regression Models Applied to the Prediction of Energy Generated by a Micro Wind Turbine. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_14

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