Hopfield-K-Means clustering algorithm: A proposal for the segmentation of electricity customers

Electric Power Systems Research

Volumn 81, Issue 2, 2011, Pages 716-724

  López, José J ^a   Aguado, José A ^a   Martin F ^a   Munoz F ^a   Rodriguez A ^a   Ruiz, José E ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Customer characterization; Electricity markets; Neural networks

Indexed keywords

CHARACTERIZATION TECHNIQUES; CUSTOMER CHARACTERIZATION; CUSTOMER CLASSIFICATION; CUSTOMER SEGMENTATION; ELECTRICITY CUSTOMERS; ELECTRICITY MARKETS; HIER-ARCHICAL CLUSTERING; HIERARCHICAL ALGORITHM; INITIAL SOLUTION; K-MEANS; K-MEANS ALGORITHM; K-MEANS CLUSTERING ALGORITHM; K-MEANS METHOD; LOCAL MINIMUMS; MODIFIED FOLLOW THE LEADERS; NUMBER OF CLUSTERS; SEGMENTATION METHODS;

CLASSIFICATION (OF INFORMATION); CUSTOMER SATISFACTION; ELECTRIC UTILITIES; RANDOM PROCESSES; RECURRENT NEURAL NETWORKS; SALES;

CLUSTERING ALGORITHMS;

EID: 78650599481     PISSN: 03787796     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.epsr.2010.10.036     Document Type: Article

References (22)

1. G. Chicco, R. Napoli, P. Postolache, M. Scutariu, and C. Toader Customer characterization options for improving the tariff offer IEEE Trans. Power Syst. 18 2003 381 387
2. G. Chicco, R. Napoli, and F. Piglione Comparisons among clustering techniques for electricity customer classification IEEE Trans. Power Syst. 21 2006 933 940
3. F. Batrinu, G. Chicco, R. Napoli, F. Piglione, P. Postolache, M. Scutariu, and C. Toader Efficient iterative refinement clustering for electricity customer classification IEEE Power Tech. 2005 27 30
4. J.J. López, F. Muñoz, A. Rodríguez, J.E. Ruiz, M. Medina, and J. Muñoz Curtosis coefficient and Hopfield recurrent neural network for classification of electricity customers Proceedings of the XI Spanish Portuguesse Conference on Electrical Engineering 2009
5. G. Chicco, R. Napoli, F. Piglione, P. Postolache, M. Scutariu, and C. Toader Emergent electricity customer classification IEE Gener. Transm. Distrib. 152 2005 164 172
6. R. Xu, and D. Wunsch Survey of clustering algorithms IEEE Trans. Neural Netw. 16 2005 645 678
7. S. Valero, M. Ortiz, S. Senabre, A. Gabaldón, and F. García Classification, filtering and identification of electrical customer load patterns through the use of Self-Organizing Maps IEEE Trans. Power Syst. 21 2006 1672 1682
8. J. McQueen Some methods for classifications and analysis of multivariate observations Proceedings of the 5th Symp. Mathematical Statics and Probability 1967 281 297
9. G.J. Tsekouras, P.B. Kotoulas, C.D. Tsirekis, E.N. Dialynas, and N.D. Hatziargyriou A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers Elsevier Electr. Power Syst. Res. 78 2008 1494 1510
10. J.J. López, J.A. Aguado, F. Martín, F. Muñoz, A. Rodríguez, and J.E. Ruiz Electric customer classification using Hopfield recurrent ANN Proceedings of the 5th International Conference on the European Electricity Market (EEM08) 2008
11. G.J. Tsekouras, N.D. Hatziargyriou, and E.N. Dialynas Two-stage pattern recognition of load curves for classification of electricity customers IEEE Trans. Power Syst. 22 2007 1120 1128
12. E. Carpaneto, G. Chicco, R. Napoli, and M. Scutariu Electricity customer classification using frequency-domain load pattern data Elsevier Electr. Power Energy Syst. 28 2006 13 20
13. S. Mallat A theory for multiresolution signal decomposition: the wavelet representation IEEE Trans. Pattern Anal. Mach. Intell. 11 1989 674 693
14. J.J. Hopfield Neural network and physical systems with emergent collective computational abilities Proc. Natl. Acad. Sci. 79 1982 2554 2558
15. J.J. Hopfield, and D. Tank Neuronal computation of decisions in optimization problems Biol. Cybernet. 52 1985 769 773
16. Y. Takefuji, and L. Chend Parallel algorithms for finding a near-maximum independent set of a circle graph IEEE Trans. Neural Netw. 1 1990 263 267
17. Y. Takefuji, K. Lee, and H. Aiso An artificial maximum neural network: a winner-take-all neuron forcing the state of the system in a solution domain Biol. Cybernet. 67 1992 243 2513
18. G. Chicco, R. Napoli, and F. Piglione Application of clustering algorithms and self organizing maps to classify electricity customers Proc. IEEE Power Tech. 2003 23 26
19. G. Chicco, R. Napoli, F. Piglione, P. Postolache, M. Scutariu, and C. Toader Load pattern-based classification of electricity customers IEEE Trans. Power Syst. 19 2004 1232 1239
20. R. Calinski, and J. Harabasz A dendrite method for cluster analysis Commun. Stat. 3 1974 1 27
21. D.L. Davies, and D.W. Bouldin A cluster separation measure IEEE Trans. Pattern Anal. Mach. Intell. 1 1979 224 227
22. G. Milligan, and M. Cooper An examination of procedures for determining the number of clusters in a data set Psychometrika 50 1985 159 179