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Multitask Support Vector Regression for Solar and Wind Energy Prediction

Author

Listed:
  • Carlos Ruiz

    (Department of Computer Engineering, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

  • Carlos M. Alaíz

    (Department of Computer Engineering, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

  • José R. Dorronsoro

    (Department of Computer Engineering, Universidad Autónoma de Madrid, 28049 Madrid, Spain
    Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

Abstract

Given the impact of renewable sources in the overall energy production, accurate predictions are becoming essential, with machine learning becoming a very important tool in this context. In many situations, the prediction problem can be divided into several tasks, more or less related between them but each with its own particularities. Multitask learning (MTL) aims to exploit this structure, training several models at the same time to improve on the results achievable either by a common model or by task-specific models. In this paper, we show how an MTL approach based on support vector regression can be applied to the prediction of photovoltaic and wind energy, problems where tasks can be defined according to different criteria. As shown experimentally with three different datasets, the MTL approach clearly outperforms the results of the common and specific models for photovoltaic energy, and are at the very least quite competitive for wind energy.

Suggested Citation

  • Carlos Ruiz & Carlos M. Alaíz & José R. Dorronsoro, 2020. "Multitask Support Vector Regression for Solar and Wind Energy Prediction," Energies, MDPI, vol. 13(23), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6308-:d:453571
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    References listed on IDEAS

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    Cited by:

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    3. Ajith, Meenu & Martínez-Ramón, Manel, 2023. "Deep learning algorithms for very short term solar irradiance forecasting: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).

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