• DocumentCode
    3313166
  • Title

    Long-Term Load Forecast Using Decision Tree Method

  • Author

    Ding, Qia

  • Author_Institution
    Dept. of PSCC, Nanjing Autom. Res. Inst.
  • fYear
    2006
  • fDate
    Oct. 29 2006-Nov. 1 2006
  • Firstpage
    1541
  • Lastpage
    1543
  • Abstract
    Accurate long-term load forecast is important in planning, especially for developing countries where the demand is increased with dynamic and high growth rate. As a data mining method, the decision tree is very useful in a sense that it allows to extract if-then rules and clarify the relationship between input and output variables easily. In this paper, a decision tree based method was used to clarify the relationship between the load and relative variables. A set of decision rules were obtained and stored in the knowledge base to forecast. Finally comparative forecasting results with a conventional approach in real system are presented, demonstrating the usefulness of the developed system
  • Keywords
    data mining; decision trees; load forecasting; power engineering computing; power system planning; data mining method; decision rules set; decision tree method; load variables; long-term load forecasting; power system planning; relative variables; Buildings; Classification tree analysis; Data mining; Decision trees; Economic forecasting; Information entropy; Load forecasting; Machinery; Power generation economics; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Systems Conference and Exposition, 2006. PSCE '06. 2006 IEEE PES
  • Conference_Location
    Atlanta, GA
  • Print_ISBN
    1-4244-0177-1
  • Electronic_ISBN
    1-4244-0178-X
  • Type

    conf

  • DOI
    10.1109/PSCE.2006.296529
  • Filename
    4075968