Artificial neural network models for wind power short-term forecasting using weather predicitons

  1. Ramírez-Rosado, I.J. 1
  2. Fernández-Jiménez, L.A. 1
  3. Monteiro, C. 2
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidade Do Porto
    info

    Universidade Do Porto

    Oporto, Portugal

    ROR https://ror.org/043pwc612

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

ISBN: 0-88986-551-5

Año de publicación: 2006

Páginas: 128-132

Tipo: Capítulo de Libro

SCOPUS: 2-s2.0-56149117887 WoS: WOS:000236371700022 GOOGLE SCHOLAR

Resumen

The use of wind energy has developed significantly worldwide. Wind power is the strongest growing form of renewable energy, ideal for a future with pollution-free electric power. But the integration of wind farms in power networks has become an important problem for the unit commitment and control of power plants in electric power systems. The intermittent nature of wind makes it difficult to forecast wind-produced electric energy in a wind farm even in the next hours. This paper compares the results obtained with a set of selected models for hourly electric power production forecasting in a real-life wind farm. The results show a significant improvement if previous numerical weather forecasts are used as input in hourly power forecasting models.