Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

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)
  • Received : 2021.09.15
  • Accepted : 2021.10.29
  • Published : 2022.03.31
Download PDF
⟨ Previous Next ⟩

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

Acknowledgement

This research was supported by the Chung-Ang University Graduate Research Scholarship in 2020, National Research Foundation of Korea (NRF) funded by the Korea government (2021R1A2B5B01001790) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20199710100060).

References

  1. Azadeh A, Saberi M, and Seraj O (2010). An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran, Energy, 35, 2351-2366 https://doi.org/10.1016/j.energy.2009.12.023
  2. Blakely L, Reno MJ, and Feng Wu-chi (2019). Spectral clustering for customer phase identification using AMI voltage timeseries, 2019 IEEE Power and Energy Conference at Illinois (PECI), 1-7
  3. Brillinger DR (2001). Time Series: Data Analysis and Theory, SIAM
  4. Chalmers C, Hurst W, Mackay M, and Fergus P (2019). Identifying behavioural changes for health monitoring applications using the advanced metering infrastructure, Behaviour & Information Technology, 38, 1154-1166. https://doi.org/10.1080/0144929X.2019.1574900
  5. Chicco G, Napoli R, Piglione F, Postolache P, Scutariu M, and Toader C (2004). Load pattern-based classification of electricity customers, IEEE Transactions on Power Systems,19, 1232-1239.
  6. Chiou J-M and Li P-L (2007). Functional clustering and identifying substructures of longitudinal data, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69, 679-699. https://doi.org/10.1111/j.1467-9868.2007.00605.x
  7. Fryzlewicz P and Ombao H (2009). Consistent classification of nonstationary time series using stochastic wavelet representations, Journal of the American Statistical Association, 104, 299-312. https://doi.org/10.1198/jasa.2009.0110
  8. Glasgo B, Hendrickson C, and Azevedo Ines ML (2017). Using advanced metering infrastructure to characterize residential energy use, The Electricity Journal, 30, 64-70.
  9. Hamilton JD (1989). A new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica: Journal of the Econometric Society, 357-384.
  10. Hubert L and Arabie P (1985). Comparing partitions, Journal of Classification, 2, 193-218. https://doi.org/10.1007/BF01908075
  11. Krishna VB, Weaver GA, and Sanders WH (2015). PCA-based method for detecting integrity attacks on advanced metering infrastructure, In Proceedings of the International Conference on Quantitative Evaluation of Systems, 70-85.
  12. Kwac JS, Flora J, and Rajagopal R (2014). Household energy consumption segmentation using hourly data, IEEE Transactions on Smart Grid, 5, 420-430. https://doi.org/10.1109/TSG.2013.2278477
  13. Likas A, Vlassis N, and Verbeek JJ (2003). The global k-means clustering algorithm, Pattern Recognition, 36, 451-461. https://doi.org/10.1016/S0031-3203(02)00060-2
  14. Mina J and Verde C (2007). Fault detection for large scale systems using dynamic principal components analysis with adaptation, International Journal of Computers Communiations & Control, 2, 185-194. https://doi.org/10.15837/ijccc.2007.2.2351
  15. Ombao H and Ho MHR (2006). Time-dependent frequency domain principal components analysis of multichannel non-stationary signals, Computational Statistics & Data Analysis, 50, 2339-2360. https://doi.org/10.1016/j.csda.2004.12.011
  16. Silverman BW and Ramsay JO (1997). Functional Data Analysis, Amsterdam, Elsevier.
  17. Romero M, Gallego L, and Pavas, Andres (2011). Estimation of voltage sags patterns with k-means algorithm and clustering of fault zones in high and medium voltage grids, Ingeniera e Investigacion, 31, 131-138. https://doi.org/10.15446/ing.investig.v31n2SUP.25224
  18. Said SE and Dickey DA (1984). Testing for unit roots in autoregressive-moving average models of unknown order, Biometrika, 71, 599-607. https://doi.org/10.1093/biomet/71.3.599
  19. Salvador M, Gallizo JL, and Gargallo P (2003). A dynamic principal components analysis based on multivariate matrix normal dynamic linear models, Journal of Forecasting, 22, 457-478. https://doi.org/10.1002/for.865
  20. Shin JH, Yi BJ, Kim YI, Lee HG, and Ryu KH (2011). Spatiotemporal load-analysis model for electric power distribution facilities using consumer meter-reading data, IEEE Transactions on Power Delivery, 26, 736-743. https://doi.org/10.1109/TPWRD.2010.2091973
  21. Shumway RH, Stoffer DS, and Stoffer DS (2000). Time Series Analysis and its Applications, 3, Springer.
  22. Van Der Linde A (2008). Variational bayesian functional PCA, Computational Statistics & Data Analysis, 53, 517-533. https://doi.org/10.1016/j.csda.2008.09.015
  23. Xu T-S, Chiang H-D, Liu G-Y and Tan C-W (2015). Hierarchical K-means method for clustering large-scale advanced metering infrastructure data, IEEE Transactions on Power Delivery, 32, 609-616. https://doi.org/10.1109/TPWRD.2015.2479941