The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Customer Clustering Method Using Repeated Small-sized Clustering to improve the Classifying Ability of Typical Daily Load Profile

일일 대표 부하패턴의 분별력을 높이기 위한 반복적인 소규모 군집화를 이용한 고객 군집화 방법

  • 김영일 (한국전력공사 전력연구원) ;
  • 송재주 (한국전력공사 전력연구원) ;
  • 오도은 (한국전력공사 전력연구원) ;
  • 정남준 (한국전력공사 전력연구원) ;
  • 양일권 (한국전력공사 전력연구원)
  • Published : 2009.11.01
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Abstract

Customer clustering method is used to make a TDLP (typical daily load profile) to estimate the quater hourly load profile of non-AMR (Automatic Meter Reading) customer. In this paper, repeated small-sized clustering method is supposed to improve the classifying ability of TDLP. K-means algorithm is well-known clustering technology of data mining. To reduce the local maxima of k-means algorithm, proposed method clusters average load profiles to small-sized clusters and selects the highest error rated cluster and clusters this to small-sized clusters repeatedly to minimize the local maxima.

Keywords

References

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