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http://dx.doi.org/10.29220/CSAM.2022.29.2.225

Clustering non-stationary advanced metering infrastructure data  

Kang, Donghyun (Department of Applied Statistics, Chung-Ang University)
Lim, Yaeji (Department of Applied Statistics, Chung-Ang University)
Publication Information
Communications for Statistical Applications and Methods / v.29, no.2, 2022 , pp. 225-238 More about this Journal
Abstract
In this paper, we propose a clustering method for advanced metering infrastructure (AMI) data in Korea. As AMI data presents non-stationarity, we consider time-dependent frequency domain principal components analysis, which is a proper method for locally stationary time series data. We develop a new clustering method based on time-varying eigenvectors, and our method provides a meaningful result that is different from the clustering results obtained by employing conventional methods, such as K-means and K-centres functional clustering. Simulation study demonstrates the superiority of the proposed approach. We further apply the clustering results to the evaluation of the electricity price system in South Korea, and validate the reform of the progressive electricity tariff system.
Keywords
advanced metering infrastructure (AMI); clustering; non-stationary data; progressive electricity tariff system; time-dependent frequency domain principal components analysis;
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