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Load Pattern-Based Classification of Electricity Customers

This research paper discusses the classification of electricity customers based on their consumption patterns, using load pattern-based classification techniques. The study compares two clustering tools, the Modified Follow-The-Leader Algorithm and Self-Organizing Maps, and assesses their performance and adequacy in customer classification.

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Load Pattern-Based Classification of Electricity Customers

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  1. 國立雲林科技大學National Yunlin University of Science and Technology • Load Pattern-Based Classification of • Electricity Customers • Advisor：Dr.Hsu • Graduate： Keng-Wei Chang • Author： Gianfranco Chicco, Roberto Napoli • Federico Piglione, Petru Postolache • Mircea Scutariu, Cornel Toader IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 19, NO.2 ,MAY 2004

  2. Outline • N.Y.U.S.T. • I.M. • Motivation • Objective • Introduction • Classification Tools and Models • Classification Adequacy Assessment • Application of The Classification Techniques • Performance Comparisons • Concluding Remarks

  3. consumption patterns for electricity providers in competitive electricity markets setting up new tariff structures more closely to the actual cost in different time periods Motivation • N.Y.U.S.T. • I.M.

  4. accurate knowledge of the customer’s consumption patterns represents Objective • N.Y.U.S.T. • I.M.

  5. face new challenges in providing satisfactory service to customers set up new tariff structures survey two classes of tools Modified Follow-The –Leader Algorithm Self-organizing maps (SOM) Introduction • N.Y.U.S.T. • I.M.

  6. rescale or resort related definition Two clustering tools Modified Follow-The-Leader Algorithm SOM Approach Classification Tools and Models • N.Y.U.S.T. • I.M.

  7. unsupervised clustering algorithm, not require initialization of the number of clusters and computes the cluster centers automatically is the variance of the hth feature of all the load patterns in the population is the average value of the variance for h=1,…,H Modified Follow-The-Leader Algorithm • N.Y.U.S.T. • I.M.

  8. N.Y.U.S.T. • I.M.

  9. unsupervised neural network, projects a H-dimensional data set into a reduced dimension space related definition N1 x N2 H-dimensional units ck, a competitive layer ||xi – ck||, activation function not only the winning unit, but also its neighbor units SOM Approach • N.Y.U.S.T. • I.M.

  10. Update the generic unit ck is the learning rate is the value of the neighborhood function referred to the generic unit k w, the identifier of the winning unit SOM Approach • N.Y.U.S.T. • I.M.

  11. General Outline and Definition of the Distances Adequacy Measures Classification Adequacy Assessment • N.Y.U.S.T. • I.M.

  12. General Outline and Definition of the Distances • N.Y.U.S.T. • I.M. • 1) the distance between two load patterns • 2) the distance between a representative load curve and subset , as the geometric mean

  13. Adequacy Measures • N.Y.U.S.T. • I.M. • Separated and compact 1) the mean index adequacy (MIA) 2) the clustering dispersion indicator (CDI)

  14. Customers of the Romanian national electricity distribution company 234 customers Over three-week time intervals Contain industrial, services, and small-business two application Application of the Modified Follow-The-Leader Algorithm Application of the SOM Application of The Classification Techniques • N.Y.U.S.T. • I.M.

  15. Application of the Modified Follow-The-Leader Algorithm • N.Y.U.S.T. • I.M. p = 2.266, k = 16

  16. Application of the Modified Follow-The-Leader Algorithm • N.Y.U.S.T. • I.M.

  17. Application of the Modified Follow-The-Leader Algorithm • N.Y.U.S.T. • I.M.

  18. Average distance from each example of the data set to its winning units Distortion of the map as the percentage of samples for which the winning unit and the second winning unit are not neighboring map units Application of the SOM • N.Y.U.S.T. • I.M.

  19. Application of the SOM • N.Y.U.S.T. • I.M.

  20. Application of the SOM • N.Y.U.S.T. • I.M. resolution property M : population N : N1 X N2 degree of utilization of the map

  21. Application of the SOM • N.Y.U.S.T. • I.M.

  22. Application of the SOM • N.Y.U.S.T. • I.M.

  23. Performance Comparisons • N.Y.U.S.T. • I.M.

  24. Performance Comparisons • N.Y.U.S.T. • I.M.

  25. both can effectively assist the customer classification Suggest using them in a way depending on the objectives Concluding Remarks • N.Y.U.S.T. • I.M.

  26. Two clustering tools Modified Follow-The-Leader Algorithm SOM Approach Classification Adequacy Assessment Application of The Classification Techniques Performance Comparisons Review • N.Y.U.S.T. • I.M.

  27. … Personal opinion • N.Y.U.S.T. • I.M.

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