Application of Classification Methods for Forecasting Mid-Term Power Load Patterns

  • Minghao Piao
  • Heon Gyu Lee
  • Jin Hyoung Park
  • Keun Ho Ryu
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)


Currently an automated methodology based on data mining techniques is presented for the prediction of customer load patterns in long duration load profiles. The proposed approach in this paper consists of three stages: (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to discrete values, (ii) cluster analysis: k-means clustering is used to create load pattern classes and the representative load profiles for each class and (iii) classification: we evaluated several supervised learning methods in order to select a suitable prediction method. According to the proposed methodology, power load measured from AMR (automatic meter reading) system, as well as customer indexes, were used as inputs for clustering. The output of clustering was the classification of representative load profiles (or classes). In order to evaluate the result of forecasting load patterns, the several classification methods were applied on a set of high voltage customers of the Korea power system and derived class labels from clustering and other features are used as input to produce classifiers. Lastly, the result of our experiments was presented.


Association Rule Data Mining Technique Load Pattern Load Forecast Load Profile 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Minghao Piao
    • 1
  • Heon Gyu Lee
    • 1
  • Jin Hyoung Park
    • 1
  • Keun Ho Ryu
    • 1
  1. 1.Database/Bioinformatics LaboratoryChungbuk National UniversityCheongjuKorea

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