• DocumentCode
    3228736
  • Title

    Long term electricity demand forecasting using autoregressive integrated moving average model: Case study of Morocco

  • Author

    Citroen, Noreddine ; Ouassaid, Mohammed ; Maaroufi, Mohamed

  • Author_Institution
    Dept. of Electr. Eng., Mohammed V Univ., Rabat, Morocco
  • fYear
    2015
  • fDate
    25-27 March 2015
  • Firstpage
    59
  • Lastpage
    64
  • Abstract
    Electricity demand forecasting is vitally important for power production companies. It has many applications, including energy production scheduling, maintenance and operation of electric network, elaboration of accurate investment and development plans for transmission and distribution networks, negotiation of PPAs (Power Purchase Agreements) and purchasing fuels at optimal costs. Over the last decades, a large variety of mathematical models have been developed for load forecasting, including short-term, medium-term and long term models. This article aims at developing a long term load forecasting model for Moroccan electric grid, using Auto-Regressive Moving Average model. The results are compared to official forecasts of ONEE (Office National de l´Eau et d´Electrcité). This model will be a useful tool for decision makers, to better design investment plans and strategies, aiming at reducing the impact of energy bill on Moroccan economy.
  • Keywords
    autoregressive moving average processes; load forecasting; Morocco; ONEE official forecast; autoregressive integrated moving average model; energy production scheduling; investment plans; load forecasting; long term electricity demand forecasting; Predictive models; Auto-Regressive; demand; electricity; forecast;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Information Technologies (ICEIT), 2015 International Conference on
  • Conference_Location
    Marrakech
  • Print_ISBN
    978-1-4799-7478-8
  • Type

    conf

  • DOI
    10.1109/EITech.2015.7162950
  • Filename
    7162950