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A New Ensemble with Partition Size Variation Applied to Wind Speed Time Series

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Hybrid Artificial Intelligent Systems (HAIS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14858))

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Abstract

Wind speed time series exhibit complex patterns, thus integrating wind energy into the electrical system is challenging. This requires specialized skills for operations and planning practices. Training expert predictors on different parts of the time series enables the identification of complex local patterns. However, the partitioning procedure reduces the number of training instances. So, investigating the size of training partitions for an ensemble is desirable for predicting wind speed. Therefore, this paper proposes a homogeneous ensemble for local pattern recognition denoted as LocPart, that varies in partition size. The results of the Diebold-Mariano hypothesis test show promise for the LocPart method applied to three wind speed time series. The comparison was made relative to individual and bagging methods that use a global mapping of the respective base model of the proposal. The LocPart method with LSTM, ARIMA, and ELM base models won in 100%, 83%, and 50% of the cases, respectively.

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Authors and Affiliations

  1. Centro de Informática (CIn), Universidade Federal de Pernambuco (UFPE), Recife, PE, Brazil

    Diogo M. Almeida, Paulo S. G. de Mattos Neto & Daniel C. Cunha

Authors
  1. Diogo M. Almeida
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  2. Paulo S. G. de Mattos Neto
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  3. Daniel C. Cunha
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Corresponding author

Correspondence to Diogo M. Almeida .

Editor information

Editors and Affiliations

  1. University of A Coruña - CITIC, Ferrol, La Coruña, Spain

    Héctor Quintián

  2. University of Salamanca, Salamanca, Spain

    Emilio Corchado

  3. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  4. University of León, León, Spain

    Hilde Pérez García

  5. University of A Coruña - CITIC, Ferrol, La Coruña, Spain

    Esteban Jove Pérez

  6. University of A Coruña - CITIC, Ferrol, La Coruña, Spain

    José Luis Calvo Rolle

  7. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  8. University of Deusto Avda, Bilbao, Spain

    Pablo García Bringas

  9. Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  10. University of Burgos, Burgos, Spain

    Álvaro Herrero

  11. University of Bergamo, Bergamo, Bergamo, Italy

    Paolo Fosci

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Almeida, D.M., Neto, P.S.G.d.M., Cunha, D.C. (2025). A New Ensemble with Partition Size Variation Applied to Wind Speed Time Series. In: Quintián, H., et al. Hybrid Artificial Intelligent Systems. HAIS 2024. Lecture Notes in Computer Science(), vol 14858. Springer, Cham. https://doi.org/10.1007/978-3-031-74186-9_5

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  • DOI: https://doi.org/10.1007/978-3-031-74186-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-74185-2

  • Online ISBN: 978-3-031-74186-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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