• DocumentCode
    1921233
  • Title

    Short- Term Electric Load Forecasting Using Neural Networks

  • Author

    Ramezani, Maryam ; Falaghi, Hamid ; Haghifam, Mahmood-Reza ; Shahryari, Gholam Ali

  • Author_Institution
    Dept. of Electr. Eng., Tarbiat Modarres Univ., Tehran
  • Volume
    2
  • fYear
    2005
  • fDate
    21-24 Nov. 2005
  • Firstpage
    1525
  • Lastpage
    1528
  • Abstract
    Artificial neural network (ANN) techniques have been recently suggested for short term electric load forecasting by a large number of researchers. This work studies the applicability of this kind of models. The work is performed for a real forecasting application. The proposed models are capable of forecasting the next 24 hour load profile, the next hour load and the next day peak load. The inputs to the ANN models are load profiles and weather information. The ANN load forecasting models are trained on historical data that obtained from a real HV/MV substation in south of Iran. Final results indicate average errors of developed models and prove that these models can be applied to the prediction of load in real case
  • Keywords
    load forecasting; neural nets; power engineering computing; ANN model; artificial neural network; historical data training; load prediction; load profile; short term electric load forecasting; substation; weather information; Artificial neural networks; Economic forecasting; Load forecasting; Load modeling; Neural networks; Power system modeling; Power system planning; Power system security; Predictive models; Weather forecasting; Artificial Neural Networks; Multi-Layer Perceptron (MLP) Neural Networks; Short-Term Electric Load Forecasting (STLF);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer as a Tool, 2005. EUROCON 2005.The International Conference on
  • Conference_Location
    Belgrade
  • Print_ISBN
    1-4244-0049-X
  • Type

    conf

  • DOI
    10.1109/EURCON.2005.1630255
  • Filename
    1630255