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

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

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

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

5. Intelligent Database Systems Lab Introduction 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) N.Y.U.S.T. I.M.

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

7. Intelligent Database Systems Lab Modified Follow-The-Leader Algorithm 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 N.Y.U.S.T. I.M.

8. Intelligent Database Systems Lab N.Y.U.S.T. I.M.

9. Intelligent Database Systems Lab SOM Approach unsupervised neural network, projects a H- dimensional data set into a reduced dimension space related definition  N 1 x N 2 H-dimensional units c k, a competitive layer  ||x i – c k ||, activation function  not only the winning unit, but also its neighbor units N.Y.U.S.T. I.M.

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

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

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

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

14. Intelligent Database Systems Lab Application of The Classification Techniques 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 N.Y.U.S.T. I.M.

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

16. Intelligent Database Systems Lab Application of the Modified Follow-The-Leader Algorithm N.Y.U.S.T. I.M.

17. Intelligent Database Systems Lab Application of the Modified Follow-The-Leader Algorithm N.Y.U.S.T. I.M.

18. Intelligent Database Systems Lab Application of the SOM 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 N.Y.U.S.T. I.M.

19. Intelligent Database Systems Lab Application of the SOM N.Y.U.S.T. I.M.

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

21. Intelligent Database Systems Lab Application of the SOM N.Y.U.S.T. I.M.

22. Intelligent Database Systems Lab Application of the SOM N.Y.U.S.T. I.M.

23. Intelligent Database Systems Lab Performance Comparisons N.Y.U.S.T. I.M.

24. Intelligent Database Systems Lab Performance Comparisons N.Y.U.S.T. I.M.

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

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

27. Intelligent Database Systems Lab Personal opinion … N.Y.U.S.T. I.M.