APPLICATION OF SELF-ORGANIZING MAPS FOR CLASSIFICATION AND FILTERING OF ELECTRICAL CUSTOMER LOAD PATTERNS

S.V. Verdu, M.O. Garc´a, C.S. Blanes, F.J.G. Franco, and A.G. Mar´n ´ ı ı

References

  1. [1] California Energy Commission, Integrated Energy Policy Report, Docket No 02-IEP-1, Pub No 100-03-019, December 2003.
  2. [2] C. Olaru & L. Wehenkel, Data mining tutorial, IEEE Computer Applications in Power, 12 (3), 1999, 19–25. doi:10.1109/67.773801
  3. [3] B.D. Pitt & D.S. Kirschen, Application of data mining techniques to load profiling, Proc. IEEE PICA’99, Santa Clara, CA, May 16–21, 1999, 131–136.
  4. [4] G. Chicco, R. Napoli, F. Piglione, P. Postolache, et al., Load pattern-based classification of electricity customers, IEEE Transactions on Power Systems, 19 (2), 2004, 1232–1238.
  5. [5] V. Figueiredo, F. Rodrigues, Z. Vale, & J.B. Gouveia, An electric energy consumer characterization framework based on data mining techniques, IEEE Transactions on Power Systems, 20, 2005, 596–602. doi:10.1109/TPWRS.2005.846234
  6. [6] F. Rosenblatt, Principles of neurodynamics (Washington DC: Spartan Press, 1961).
  7. [7] T. Kohonen, Self-organisation and associative memory, Third Edition (Berlin: Springer-Verlag, 1989).
  8. [8] J.L. Elman, Finding structure in time, Cognitive Science, 14, 1990, 179–211. doi:10.1016/0364-0213(90)90002-E
  9. [9] S. Chen, C.F.N. Cowan, & P.M. Grant, Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks, 2 (2), 1991, 302–309. doi:10.1109/72.80341
  10. [10] D. Gerbec, S. Gasperic, I. Smon, & F. Gubina, Allocation of the load profiles to consumers using probabilistic neural networks, IEEE Transactions on Power Systems, 20, 2005, 548–555. doi:10.1109/TPWRS.2005.846236
  11. [11] J.-S.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man and Cybernetics, 23, 1993, 665–685. doi:10.1109/21.256541
  12. [12] S. Valero, M. Ortiz, Fco. Garc´ıa, A. Gabald´on, et al., Characterization and identification of electrical customer through the use of SOM and daily load parameters, IEEE PSCE2004, New York, October 10–13, 2004.
  13. [13] G. Chicco, R. Napoli, & F. Piglione, Load pattern clustering for short-term load forecasting of anomalous days, 2001 IEEE Porto Power Tech Conf., 10–13 September, Porto, Portugal.
  14. [14] C.S. Chen, J.C. Hwang, & Y.M. Zzeng, C.W. Huang, & M.Y. Cho, Determination of customer load characteristics by load survey system at taipower, IEEE Transactions on Power Delivery, 11 (3), July 1996, 1430–1436.
  15. [15] R. Lamedica, A. Prudenzi, M. Sforna, M. Caciotta, et al., A neural network based technique for short-term load forecasting of anomalous load periods, IEEE Transactions on Power Systems, 11 (4) 1996, 1749–1756. 89

Important Links:

Go Back