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http://dx.doi.org/10.9723/jksiis.2013.18.3.035

Forecasting Electric Power Demand Using Census Information and Electric Power Load  

Lee, Heon Gyu (한국전자통신연구원 융합기술연구부문)
Shin, Yong Ho (영남대학교 경영학부)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.18, no.3, 2013 , pp. 35-46 More about this Journal
Abstract
In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.
Keywords
subspace clustering; census information; power demand forecasting; microarea clustering; data mining;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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