Journal of Electrical Engineering and Technology

  • 제7권3호
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  • Pages.281-287
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  • 2012
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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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)
  • 투고 : 2010.12.29
  • 심사 : 2012.01.31
  • 발행 : 2012.05.01
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초록

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.

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참고문헌

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피인용 문헌

  1. Subspace Projection Method Based Clustering Analysis in Load Profiling vol.29, pp.6, 2014, https://doi.org/10.1109/TPWRS.2014.2309697