Short-term forecasting model for aggregated regional hydropower generation

  1. Monteiro, C. 3
  2. Ramirez-Rosado, I.J. 2
  3. Fernandez-Jimenez, L.A. 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

  3. 3 Universidade Do Porto
    info

    Universidade Do Porto

    Oporto, Portugal

    ROR https://ror.org/043pwc612

Revista:
Energy Conversion and Management

ISSN: 0196-8904

Año de publicación: 2014

Volumen: 88

Páginas: 231-238

Tipo: Artículo

DOI: 10.1016/J.ENCONMAN.2014.08.017 SCOPUS: 2-s2.0-84907528332 WoS: WOS:000345725400024 GOOGLE SCHOLAR

Otras publicaciones en: Energy Conversion and Management

Resumen

This paper presents an original short-term forecasting model of the hourly electric power production for aggregated regional hydropower generation. The inputs of the model are previously recorded values of the aggregated hourly production of hydropower plants and hourly water precipitation forecasts using Numerical Weather Prediction tools, as well as other hourly data (load demand and wind generation). This model is composed of three modules: the first one gives the prediction of the "monthly" hourly power production of the hydropower plants; the second module gives the prediction of hourly power deviation values, which are added to that obtained by the first module to achieve the final forecast of the hourly hydropower generation; the third module allows a periodic adjustment of the prediction of the first module to improve its BIAS error. The model has been applied successfully to the real-life case study of the short-term forecasting of the aggregated hydropower generation in Spain and Portugal (Iberian Peninsula Power System), achieving satisfactory results for the next-day forecasts. The model can be valuable for agents involved in electricity markets and useful for power system operations.