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Prediction of Wind Speed Using Hybrid Techniques

Author

Listed:
  • Luis Lopez

    (Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
    These authors are affiliated with the Universidad del Norte.
    These authors contributed equally to this work.)

  • Ingrid Oliveros

    (Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
    These authors are affiliated with the Universidad del Norte.
    These authors contributed equally to this work.)

  • Luis Torres

    (Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
    These authors are affiliated with the Universidad del Norte.
    These authors contributed equally to this work.)

  • Lacides Ripoll

    (Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
    These authors are affiliated with the Universidad del Norte.
    These authors contributed equally to this work.)

  • Jose Soto

    (Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
    These authors are affiliated with the Universidad del Norte.
    These authors contributed equally to this work.)

  • Giovanny Salazar

    (Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
    These authors contributed equally to this work.)

  • Santiago Cantillo

    (Electrical and Electronics Department, Universidad del Norte, Km 5 Via Pto Colombia, Barranquilla 081007, Colombia
    These authors contributed equally to this work.)

Abstract

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: (1) As a single time series containing all measurements, and (2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 h in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. Moreover, the traditional and simple auto-regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square error (RMSE).

Suggested Citation

  • Luis Lopez & Ingrid Oliveros & Luis Torres & Lacides Ripoll & Jose Soto & Giovanny Salazar & Santiago Cantillo, 2020. "Prediction of Wind Speed Using Hybrid Techniques," Energies, MDPI, vol. 13(23), pages 1-13, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6284-:d:452986
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    References listed on IDEAS

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    1. Riccardo De Blasis & Giovanni Batista Masala & Filippo Petroni, 2021. "A Multivariate High-Order Markov Model for the Income Estimation of a Wind Farm," Energies, MDPI, vol. 14(2), pages 1-16, January.

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