Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Repeated Clustering to Improve the Discrimination of Typical Daily Load Profile

  • Kim, Young-Il (S/W Center, KEPCO (Korea Electric Power Cooperation) Research Institute, KEPCO) ;
  • Ko, Jong-Min (S/W Center, KEPCO Research Institute, KEPCO) ;
  • Song, Jae-Ju (S/W Center, KEPCO Research Institute, KEPCO) ;
  • Choi, Hoon (Dept. of Computer Engineering, Chungnam National University)
  • Received : 2010.12.29
  • Accepted : 2012.01.31
  • Published : 2012.05.01
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Abstract

The customer load profile clustering method is used to make the TDLP (Typical Daily Load Profile) to estimate the quarter hourly load profile of non-AMR (Automatic Meter Reading) customers. This study examines how the repeated clustering method improves the ability to discriminate among the TDLPs of each cluster. The k-means algorithm is a well-known clustering technology in data mining. Repeated clustering groups the cluster into sub-clusters with the k-means algorithm and chooses the sub-cluster that has the maximum average error and repeats clustering until the final cluster count is satisfied.

Keywords

References

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