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http://dx.doi.org/10.5351/KJAS.2021.34.6.969

Clustering load patterns recorded from advanced metering infrastructure  

Ann, Hyojung (Department of Statistics, The Graduate School of Chung-Ang University)
Lim, Yaeji (Department of Statistics, The Graduate School of Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.6, 2021 , pp. 969-977 More about this Journal
Abstract
We cluster the electricity consumption of households in A-apartment in Seoul, Korea using Hierarchical K-means clustering algorithm. The data is recorded from the advanced metering infrastructure (AMI), and we focus on the electricity consumption during evening weekdays in summer. Compare to the conventional clustering algorithms, Hierarchical K-means clustering algorithm is recently applied to the electricity usage data, and it can identify usage patterns while reducing dimension. We apply Hierarchical K-means algorithm to the AMI data, and compare the results based on the various clustering validity indexes. The results show that the electricity usage patterns are well-identified, and it is expected to be utilized as a major basis for future applications in various fields.
Keywords
advanced metering infrastructure; clustering algorithm; clustering validity index; Hierarchical K-means clustering; power consumption data;
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