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Repeated Clustering to Improve the Discrimination of Typical Daily Load Profile
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 Title & Authors
Repeated Clustering to Improve the Discrimination of Typical Daily Load Profile
Kim, Young-Il; Ko, Jong-Min; Song, Jae-Ju; Choi, Hoon;
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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
Repeated clustering;K-means;Typical load profile;Discrimination;Cosine similarity;
 Language
English
 Cited by
1.
Subspace Projection Method Based Clustering Analysis in Load Profiling, IEEE Transactions on Power Systems, 2014, 29, 6, 2628  crossref(new windwow)
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