• DocumentCode
    1793195
  • Title

    An ARCH model the electric power of extra high voltage (EHV) transmission substation forecasting in Cawang, Jakarta, Indonesia

  • Author

    Pasaribu, U.S. ; Setiyowati, Susi ; Mukhaiyar, Utriweni

  • Author_Institution
    Fac. of Math. & Natural Sci., Bandung Inst. of Technol., Bandung, Indonesia
  • fYear
    2014
  • fDate
    19-21 Aug. 2014
  • Firstpage
    52
  • Lastpage
    57
  • Abstract
    An ARCH forecasting model for electric power demand of an EHV is proposed in this work. The power demand in EHV substation, Jakarta, Indonesia which passed through Cawang substation is continuously changed in values either normally or drastically. These electric demands were measured every 30 minutes so that there are 48 different values of electric demand that pass through a substation for one day. To distribute the power to the customers, PT Perusahaan Listrik Negara (PLN) has to determine which power plant will be used with the most optimum route and minimum operations budget. For that purpose, an ARCH modeling was applied so that the forecast power demand could be estimated. This model is applied to unveil the heteroscedasticity character of residuals with inconstant variance. In this work, MATLAB software and NUMXL were used for parameters estimation of model identification. As a result, it turns out that convergent parameter values obtained when 28 data observations were used. Therefore, the forecasting of electric power was generated in order to forecast up to ten steps ahead. This forecasting of the electric power could provide extensive and valuable information of electric power to PLN so that electric route and related budget can be optimized.
  • Keywords
    load forecasting; power system parameter estimation; substations; ARCH forecasting model; Cawang; Indonesia; Jakarta; MATLAB software; NUMXL; PT Perusahaan Listrik Negara; electric power demand; extra high voltage transmission substation forecasting; model identification; parameters estimation; Data models; Electricity; Forecasting; Predictive models; Substations; Time series analysis; ARCH; EHV (Extra High Voltage); forecasting; heteroscedasticity; non-stationary time series model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Autonomous Agents, Networks and Systems (INAGENTSYS), 2014 IEEE International Conference on
  • Conference_Location
    Bandung
  • Print_ISBN
    978-1-4799-4803-1
  • Type

    conf

  • DOI
    10.1109/INAGENTSYS.2014.7005725
  • Filename
    7005725