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Customer Load Pattern Analysis using Clustering Techniques
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 Title & Authors
Customer Load Pattern Analysis using Clustering Techniques
Ryu, Seunghyoung; Kim, Hongseok; Oh, Doeun; No, Jaekoo;
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 Abstract
Understanding load patterns and customer classification is a basic step in analyzing the behavior of electricity consumers. To achieve that, there have been many researches about clustering customers` daily load data. Nowadays, the deployment of advanced metering infrastructure (AMI) and big-data technologies make it easier to study customers` load data. In this paper, we study load clustering from the view point of yearly and daily load pattern. We compare four clustering methods; K-means clustering, hierarchical clustering (average & Ward`s method) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). We also discuss the relationship between clustering results and Korean Standard Industrial Classification that is one of possible labels for customers` load data. We find that hierarchical clustering with Ward`s method is suitable for clustering load data and KSIC can be well characterized by daily load pattern, but not quite well by yearly load pattern.
 Keywords
Load profile;load pattern clustering;industrial classification;K-means clustering;hierarchical clustering;DBSCAN;
 Language
Korean
 Cited by
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