The Influence on Clustering Results of Electricity Load Curves Using Different Distances

Abstract:

Distance choice is an important issue in power load pattern extraction using clustering techniques, so it is necessary to find the influence on clustering result of load curves using different distances in clustering algorithms. In this paper several distances are used in the k-means algorithm for clustering load curves and their influences on the clustering results are analyzed, therefore, the suitable distance for the k-means algorithms is obtained. An example with 147 electricity customers load curves shows distances have different influences on clustering results using the same clustering algorithm. The comparison results indicate that the choice of distances is an important issue in power load pattern extraction using clustering techniques and a suitable distance may improve the accuracy of mining algorithms.

• Periodical: Applied Mechanics and Materials (Volumes 401-403)
• Pages: 1440-1443
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.401-403.1440
• Online since: September 2013
• Authors: Tie Feng Zhang, Fei Lv, Rong Gu
• Keywords: Clustering, Distance, K-Means Algorithm

References

[1] L.Q. Liu, Q.L. Ding T.F. Zhang, Data pretreatment method of fuzzy c-means clustering effects, Electric Power Science and Engineering , 27(2011)24-27.

[2] H. T. Yang, S. C. Chen and P. C. Peng, Genetic k-means-algorithm-based classification of direct load-control curves, IEEE Proceedings-Generation, Transmission and Distribution, 152(2005)489-495.

DOI: 10.1049/ip-gtd:20045006

[3] S. Valero, M. Ortiz, C. Senabre, C. Alvarez, F. J. G. Franco and A. Gabaldon, Methods for customer and demand response policies selection in new electricity markets, IET Generation, Transmission & Distribution, 1(2007)104-110.

DOI: 10.1049/iet-gtd:20060183

[4] M. Espinoza, C. Joye, R. Belmans and B. DeMoor, Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series, IEEE Transactions on Power Systems, 20(2005)1622-1630.

DOI: 10.1109/tpwrs.2005.852123

[5] T.F. Zhang, G.Q. Zhang, J. Lu, X.P. Feng, W.C. Yang. A New Index and Classification Approach for Load Pattern Analysis of Large Electricity Customers, IEEE Transactions on Power Systems, 27(2012)153-160.

DOI: 10.1109/tpwrs.2011.2167524

[6] J.A. Hartigan and M.A. Wong, Algorithm AS 136: a k-means clustering algorithm, Journal of the Royal Statistical SocietySeries C (Applied Statistics), 28(1979)100-108.

DOI: 10.2307/2346830

[7] E. Carpaneto, G. Chicco, R. Napoli and M. Scutariu, Electricity customer classification using frequency-domain load pattern data, International Journal of Electrical Power & Energy Systems, 28(2006)13-20.

DOI: 10.1016/j.ijepes.2005.08.017

[8] G. J. Tsekouras, N. D. Hatziargyriou and E. N. Dialynas, Two-stage pattern recognition of load curves for classification of electricity customers, IEEE Transactions on Power Systems, 22(2007)1120-1128.

DOI: 10.1109/tpwrs.2007.901287

[9] W.Y. Li, J.Q. Zhou, X.F. Xiong and J.P. Lu, A statistic-fuzzy technique for clustering load curves, IEEE Transactions on Power Systems, 22(2007）890-891.

DOI: 10.1109/tpwrs.2007.894851

[10] S. V. Verdu, M. O. Garcia, C. Senabre, A. G. Marin and F. J. G. Franco, Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps, IEEE Transactions on Power Systems, 21(2006).

DOI: 10.1109/tpwrs.2006.881133

[11] N. M. Kohan, M. P. Moghaddam, S. M. Bidaki and G. R. Yousefi, Comparison of modified k-means and hierarchical algorithms in customers load curves clustering for designing suitable tariffs in electricity market, in Proc. 43rd International Universities Power Engineering Conference, Padova, 28 (2008).

DOI: 10.1109/upec.2008.4651519

[12] Z. Zakaria, K. L. Lo and M. H. Sohod, Application of fuzzy clustering to determine electricity consumers' load profiles, in Proc. IEEE International Power and Energy Conference, Putra Jaya, Malaysia, 29(2006)99-103.

DOI: 10.1109/pecon.2006.346627