• DocumentCode
    2804544
  • Title

    Standardization of short-term load forecasting models

  • Author

    López, M. ; Valero, S. ; Senabre, C. ; Aparicio, J. ; Gabaldon, A.

  • Author_Institution
    Dipt. Ing. de Sist. Ind., Univ. Miguel Hernandez de Elche (UMH), Elche, Spain
  • fYear
    2012
  • fDate
    10-12 May 2012
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    There has been a significant production of load forecasting models over the last 5 years. These models present a wide variety of techniques, most of them using novel artificial intelligence approaches. Load forecasting is a complex matter and it is the result of several processes that, depending on the database, may be of more or less importance. However, most models focus their attention only on one process like the “forecasting engine”, neglecting other processes like variable selection or pre-processing. This paper proposes a standard scheme for load forecasting models that includes all sub-processes within load forecasting. The analysis of load forecasting models through this scheme allows identifying the effect of each process on the overall performance of the model. Also, proposing load forecasting models following this scheme will enhance benchmarking possibilities and hybridization of models. Finally, this paper presents such analysis of an actual load forecasting model.
  • Keywords
    artificial intelligence; load forecasting; power engineering computing; artificial intelligence approaches; benchmarking possibility enhancement; database; forecasting engine; short-term load forecasting models; Databases; Engines; Forecasting; Load forecasting; Load modeling; Predictive models; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    European Energy Market (EEM), 2012 9th International Conference on the
  • Conference_Location
    Florence
  • Print_ISBN
    978-1-4673-0834-2
  • Electronic_ISBN
    978-1-4673-0832-8
  • Type

    conf

  • DOI
    10.1109/EEM.2012.6254733
  • Filename
    6254733