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http://dx.doi.org/10.5370/JEET.2012.7.6.807

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)
Publication Information
Journal of Electrical Engineering and Technology / v.7, no.6, 2012 , pp. 807-813 More about this Journal
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.
Keywords
Data mining; K-mean algorithm; k-NN algorithm; Short-term load forecasting; Time series forecasting;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By Web Of Science : 0  (Related Records In Web of Science)
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