Short-term Electric Load Forecasting Using Data Mining Technique

被引：12
|
作者：
Kim, Cheol-Hong [1 ]
Koo, Bon-Gil [1 ]
Park, June Ho [1 ]
机构：
[1] Pusan Natl Univ, Dept Elect & Elect Engn, Sch Elect Engn, Pusan, South Korea
来源：
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY | 2012年 / 7卷 / 06期
关键词：
Data mining; K-mean algorithm; k-NN algorithm; Short-term load forecasting; Time series forecasting;
D O I：
10.5370/JEET.2012.7.6.807
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：807 / 813
页数：7
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