• DocumentCode
    2837331
  • Title

    Annual Electricity Consumption Forecasting with Neural Network in High Energy Consuming Industrial Sectors of Iran

  • Author

    Azadeh, M. Ali ; Sohrabkhani, Sara

  • Author_Institution
    Tehran Univ., Tehran
  • fYear
    2006
  • fDate
    15-17 Dec. 2006
  • Firstpage
    2166
  • Lastpage
    2171
  • Abstract
    Due to various changes in electricity consumption in Iran, it is hard to model with conventional methods and makes it suitable to estimate with Artificial Neural Network. Altough this method typically has been used to forecast short term consumptions, we use Neural Network to forecast annual consumption This paper illustrates an Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network for the electrical consumption forecasting and shows that it can estimate the annual consumption with lesser error. This study shows the advantage of Neural Network methodology through analysis of variance (ANOVA). Furthermore, actual data is compared with ANN and conventional regression model.
  • Keywords
    load forecasting; multilayer perceptrons; power engineering computing; ANN; Iran; MLP; annual electricity consumption forecasting; artificial neural network; electrical analysis of variance; high energy consuming industrial sectors; supervised multi layer perceptron; Artificial neural networks; Chemical industry; Construction industry; Electrical equipment industry; Energy consumption; Load forecasting; Manufacturing industries; Metals industry; Neural networks; Predictive models;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Technology, 2006. ICIT 2006. IEEE International Conference on
  • Conference_Location
    Mumbai
  • Print_ISBN
    1-4244-0726-5
  • Electronic_ISBN
    1-4244-0726-5
  • Type

    conf

  • DOI
    10.1109/ICIT.2006.372572
  • Filename
    4237894