• DocumentCode
    1281033
  • Title

    Australian Electricity Market

  • Author

    Wang, Xiongfei ; Hatziargyriou, Nikos ; Tsoukalas, L.H.

  • Author_Institution
    Purdue Univ., West Lafayette, IN, USA
  • Volume
    22
  • Issue
    5
  • fYear
    2002
  • fDate
    5/1/2002 12:00:00 AM
  • Firstpage
    4
  • Lastpage
    15
  • Abstract
    A neurofuzzy methodology for online nodal load prediction is introduced that exploits the power of artificial neural networks (ANN) and fuzzy logic. ANNs are used to capture the power consumption patterns specific to a customer, while a fuzzy logic module detects departures from equilibrium (that is, previously established consumption patterns). The fuzzy-logic-based (FL) module (called PROTREN) performs signal trend identification. The proposed methodology improves the adaptability of the forecasting system to sudden changes or special events that may influence the load by temporarily distorting the general pattern and thus rendering the load signal highly unpredictable. Experiments have been performed to verify the effectiveness of the new methodology. Results show that the methodology has a better performance than those using traditional forecasting methodologies, especially when special events influencing the load occur.
  • Keywords
    fuzzy logic; load forecasting; neural nets; power consumption; power system analysis computing; PROTREN; artificial neural networks; deregulated power systems; fuzzy logic; fuzzy-logic-based module; neurofuzzy methodology; nodal load forecasting; online nodal load prediction; power consumption patterns; signal trend identification; Artificial neural networks; Energy management; Fuzzy logic; Load forecasting; Power engineering; Power system dynamics; Power system management; Power system modeling; Power systems; Weather forecasting;
  • fLanguage
    English
  • Journal_Title
    Power Engineering Review, IEEE
  • Publisher
    ieee
  • ISSN
    0272-1724
  • Type

    jour

  • DOI
    10.1109/39.999661
  • Filename
    999661