Data mining technique using the coefficient of determination in holiday load forecasting

Young Min Wi, Kyung Bin Song, Sung Kwan Joo

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)18-22
Number of pages5
JournalTransactions of the Korean Institute of Electrical Engineers
Issue number1
Publication statusPublished - 2009 Jan


  • Coefficient of determination
  • Load forecasting
  • Polynomial regression

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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