Short-Term Power Demand Forecasting Using Information Technology Based Data Mining Method

  • Sang-Yule Choi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3984)


This paper proposes information technology based data mining to forecast short term power demand. A time-series analyses have been applied to power demand forecasting, but this method needs not only heavy computational calculation but also large amount of coefficient data. Therefore, it is hard to analyze data in fast way. To overcome time consuming process, the author take advantage of universally easily available information technology based data-mining technique to analyze patterns of days and special days(holidays, etc.). This technique consists of two steps, one is constructing decision tree, the other is estimating and forecasting power flow using decision tree analysis. To validate the efficiency, the author compares the estimated demand with real demand from the Korea Power Exchange.


Power Demand Demand Forecast Load Demand Demand Pattern Decision Tree Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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    Cho, S.-s., Hwang, S.-y., Lee, G.-h.: Time series analysis. Korea National Open University press (2001)Google Scholar
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    KPX-SNU Load Forecaster: Korea power exchanger(KPX). Seoul National University (December 2002)Google Scholar
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    Hwang, K.-J., Kim, K.-H., Kim, S.-H.: Development of a Weekly Load Forecasting Expert System, vol. 48(4), pp. 365–370. KIEE press (April 1999)Google Scholar
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    Han, J.: Data Mining: Concepts and Techniques. Elsevier, Singapore (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sang-Yule Choi
    • 1
  1. 1.Dept.of Electronic EngineeringInduk Institute of TechnologySeoulSouth Korea

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