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Customer Clustering Method Using Repeated Small-sized Clustering to improve the Classifying Ability of Typical Daily Load Profile  

Kim, Young-Il (한국전력공사 전력연구원)
Song, Jae-Ju (한국전력공사 전력연구원)
Oh, Do-Eun (한국전력공사 전력연구원)
Jung, Nam-Joon (한국전력공사 전력연구원)
Yang, Il-Kwon (한국전력공사 전력연구원)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.58, no.11, 2009 , pp. 2269-2274 More about this Journal
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
Customer clustering; K-means; Typical Load Profile; AMR (Automatic Meter Reading);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By SCOPUS : 0
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