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Multivariate time series clustering of electricity consumption data
Journal of the Korean Data & Information Science Society 2021;32:569-84
Published online May 31, 2021;
© 2021 Korean Data and Information Science Society.

Inhee Kim1 · Jaehee Kim2

1Department of Mathematics and Statistics, Duksung Women’s University
2Department of Statistics, Duksung Women’s University
Correspondence to: This research was supported by Korea Electric Power Corporation (Grant number: R18XA01). Also it is supported by the Korea Research Foundation (KNRF) (No. 2018R1A2B26001664).
1 Master, Department of Mathematics and Statistics, Duksung Women’s University, Seoul 01369, Korea.
2 Corresponding author: Professor, Department of Statistics, DuksungWomen’s University, Seoul 01369, Korea. E-mail:
Received February 17, 2021; Revised March 16, 2021; Accepted March 16, 2021.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
With the development of smart grids, access to electricity data is easy, and large amounts of electricity data can be obtained. Since the electricity demand generally has patterns, it is effective to cluster and predict the demand in each cluster. Therefore, in this paper, we perform a time series cluster analysis on the electricity demand. We used five distance measures and three clustering methods. Four cluster validation metrics for cluster evaluation are computed and compared. . Simulation study is done with the generated data from various patterns. Also clustering methods are applied to real electricity usage data.
Keywords : Cluster analysis, cluster Validity, electricity consumption, multivariate time series.