• DocumentCode
    3730206
  • Title

    Prediction of Iran´s annual electricity demand: Artificial intelligence approaches

  • Author

    Homayoun Hamed Moghadam Rafati;Mahdi Jalili;Hamed Davari;Reza Maknoon

  • Author_Institution
    Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
  • fYear
    2015
  • Firstpage
    373
  • Lastpage
    377
  • Abstract
    Accurate prediction of electricity demand is essential for planning, policy making and resource allocation in national level. In this manuscript, we applied a number of artificial intelligence methods to predict macro-scale electricity consumption rates in Iran. To this end, three socio-economic and three environmental factors were considered as inputs to the prediction models. We used data for the period 1967-2013 in order to predict the power demand in the years 2014-2018. Experimental results showed that the path coefficient analysis model with linear coefficients had the best performance among the models considered in this study. The outcome of this research can help the policy makers to better understand the mark needs.
  • Keywords
    "Biological system modeling","Sociology","Predictive models","Mathematical model","Training","Principal component analysis"
  • Publisher
    ieee
  • Conference_Titel
    Innovations in Information Technology (IIT), 2015 11th International Conference on
  • Print_ISBN
    978-1-4673-8509-1
  • Type

    conf

  • DOI
    10.1109/INNOVATIONS.2015.7381570
  • Filename
    7381570