Data Mining Technique Using the Coefficient of Determination in Holiday Load Forecasting

특수일 최대 전력 수요 예측을 위한 결정계수를 사용한 데이터 마이닝

  • 위영민 (고려대학교 전기전자전파학과) ;
  • 송경빈 (숭실대학교 전기공학) ;
  • 주성관 (고려대학교 전기전자전파공학부)
  • Published : 2009.01.01

Abstract

Short-term load forecasting (STLF) is an important task in power system planning and operation. Its accuracy affects the reliability and economic operation of power systems. STLF is to be classified into load forecasting for weekdays, weekends, and holidays. Due to the limited historical data available, it is more difficult to accurately forecast load for holidays than to forecast load for weekdays and weekends. It has been recognized that the forecasting errors for holidays are large compared with those for weekdays in Korea. This paper presents a polynomial regression with data mining technique to forecast load for holidays. In statistics, a polynomial is widely used in situations where the response is curvilinear, because even complex nonlinear relationships can be adequately modeled by polynomials over a reasonably small range of the dependent variables. In the paper, the coefficient of determination is proposed as a selection criterion for screening weekday data used in holiday load forecasting. A numerical example is presented to validate the effectiveness of the proposed holiday load forecasting method.

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