• English
    • español
  • español 
    • English
    • español
  • Login
Ver ítem 
  •   DSpace Principal
  • 2.- Investigación
  • Documentos de Trabajo
  • Ver ítem
  •   DSpace Principal
  • 2.- Investigación
  • Documentos de Trabajo
  • Ver ítem
JavaScript is disabled for your browser. Some features of this site may not work without it.

EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models

Thumbnail
Ver/
IIT-17-078A.pdf (385.4Kb)
Autor
Dimoulkas, Ilias
Mazidi, Peyman
Herre, Lars Finn
Estado
info:eu-repo/semantics/draft
Metadatos
Mostrar el registro completo del ítem
Mostrar METS del ítem
Ver registro en CKH

Refworks Export

Resumen
 
 
Energy forecasting provides essential contribution to integrate renewable energy sources into power systems. Today,renewable energy from wind power is one of the fastest growing means of power generation. As wind power forecast accuracy gains growing significance, the number of models used for forecasting is increasing as well. In this paper, we propose an autoregressive (AR) model that can be used as a benchmark model to validate and rank different forecasting models and their accuracy. The presented paper and research was developed within the scope of the European energy market (EEM) 2017 wind power forecasting competition.
 
URI
http://hdl.handle.net/11531/18256
EEM 2017 Forecast Competition: Wind power generation prediction using autoregressive models
Palabras Clave


Colecciones
  • Documentos de Trabajo

Repositorio de la Universidad Pontificia Comillas copyright © 2015  Desarrollado con DSpace Software
Contacto | Sugerencias
 

 

Búsqueda semántica (CKH Explorer)


Listar

Todo DSpaceComunidades & ColeccionesPor fecha de publicaciónAutoresTítulosMateriasPor DirectorPor tipoEsta colecciónPor fecha de publicaciónAutoresTítulosMateriasPor DirectorPor tipo

Mi cuenta

AccederRegistro

Repositorio de la Universidad Pontificia Comillas copyright © 2015  Desarrollado con DSpace Software
Contacto | Sugerencias