Prediction of Small-Wind Turbine Performance from Time Series Modelling Using Intelligent Techniques

  1. Santiago Porras 1
  2. Esteban Jove 2
  3. Bruno Baruque 1
  4. José Luis Calvo-Rolle 2
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Liburua:
Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings
  1. Cesar Analide (ed. lit.)
  2. Paulo Novais (ed. lit.)
  3. David Camacho (ed. lit.)
  4. Hujun Yin (ed. lit.)

Argitaletxea: Springer International Publishing AG

ISBN: 978-3-030-62362-3,  978-3-030-62361-6,  978-3-030-62364-7,  978-3-030-62365-4

Argitalpen urtea: 2020

Bolumenaren izenburua: Part II

Alea: 2

Orrialdeak: 541-548

Biltzarra: Intelligent Data Engineering and Automated Learning – IDEAL (21. 2020. Guimarães)

Mota: Biltzar ekarpena

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Laburpena

The present research work deals the model creation obtaining for power generation prediction of a small-wind turbine, based on the atmospheric variables of its location. For testing purposes, a real dataset has been obtained of a bio-climate house located in Sotavento Experimental Wind Farm in the north of Spain. A deep study of the system and atmospheric variables has been performed. Then, some different regression techniques have been tested for accomplishing prediction, obtaining excellent results.

Datuen iturria: Dialnet