Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Short-term Electric Load Forecasting Using Data Mining Technique

  • Kim, Cheol-Hong (Kyungnam energy co., LTD.) ;
  • Koo, Bon-Gil (Dept. of Electric and Electronic Engineering, Pusan National University) ;
  • Park, June-Ho (Dept. of Electric and Electronic Engineering, Pusan National University)
  • Received : 2011.02.07
  • Accepted : 2012.05.24
  • Published : 2012.11.01
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Abstract

In this paper, we introduce data mining techniques for short-term load forecasting (STLF). First, we use the K-mean algorithm to classify historical load data by season into four patterns. Second, we use the k-NN algorithm to divide the classified data into four patterns for Mondays, other weekdays, Saturdays, and Sundays. The classified data are used to develop a time series forecasting model. We then forecast the hourly load on weekdays and weekends, excluding special holidays. The historical load data are used as inputs for load forecasting. We compare our results with the KEPCO hourly record for 2008 and conclude that our approach is effective.

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