Wavelet decomposition and neuro-fuzzy hybrid system applied to short-term wind power forecasting

  1. Fernandez-Jimenez, L.A. 1
  2. Ramirez-Rosado, I.J. 2
  3. Abebe, B. 2
  4. Mendoza-Villena, M. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad de Zaragoza
    info

    Universidad de Zaragoza

    Zaragoza, España

    ROR https://ror.org/012a91z28

Libro:
Proceedings of the IASTED International Conference on Modelling, Identification and Control

Editorial: ACTA Press

ISBN: 978-0-88986-819-9

Año de publicación: 2010

Páginas: 333-338

Tipo: Capítulo de Libro

SCOPUS: 2-s2.0-77954572834 GOOGLE SCHOLAR

Resumen

This paper presents a new statistical short-term wind power forecasting model based on wavelet decomposition and neuro-fuzzy systems optimized with a genetic algorithm. The forecasted variable is the mean electric power production in a wind farm corresponding to half hour intervals. The forecasting horizons range from 0.5 to 4 hours. The optimization process, ruled by the genetic algorithm, selects the proper inputs and the parameters needed by a clustering algorithm to obtain after training, the neuro-fuzzy system with the lowest forecasting errors. The forecasting results obtained with the final models have been compared to those obtained with traditional forecasting models showing a better performance for all the forecasting horizons.