A hybrid neural network model and encoding technique for enhanced classification of energy consumption data

IEEE Power and Energy Society General Meeting

Volumn , Issue , 2011, Pages

  Depuru, Soma Shekara Sreenadh Reddy ^a   Wang, Lingfeng ^a   Devabhaktuni, Vijay ^a   Nelapati, Praneeth ^a  

^a UNIVERSITY OF TOLEDO   (United States)

Author keywords

Data classification; electricity theft; encoding; neural networks; power consumption patterns and support vector machines

Indexed keywords

CLASSIFICATION EFFICIENCY; CLASSIFICATION OF DATA; CUSTOMER LOAD PROFILES; DATA CLASSIFICATION; ELECTRICAL ENERGY; ELECTRICITY CONSUMPTION PATTERNS; ELECTRICITY THEFT; ENCODING TECHNIQUES; HIERARCHICAL MODEL; HYBRID NEURAL NETWORKS; LOAD PROFILES; NEURAL NETWORK MODEL; NON-TECHNICAL LOSS; REAL TIME; TOTAL LOSS; TRAINING FUNCTION; TRANSMISSION AND DISTRIBUTION; UTILITY COMPANIES;

CLASSIFICATION (OF INFORMATION); CRIME; CUSTOMER SATISFACTION; ELECTRIC UTILITIES; ENCODING (SYMBOLS); ENERGY UTILIZATION; HIERARCHICAL SYSTEMS; NEURAL NETWORKS; PARAMETER ESTIMATION; SALES; SUPPORT VECTOR MACHINES;

NETWORK CODING;

EID: 82855182204     PISSN: 19449925     EISSN: 19449933     Source Type: Conference Proceeding    
DOI: 10.1109/PES.2011.6039050     Document Type: Conference Paper

References (40)

1. M.A. Oliveira and C.C. Barioni, "Technical loss calculation by distribution system segment with corrections from measurements," Proc. 20th International Conference and Exhibition on Electricity Distribution, Prague, Czech Republic, June 2009, pp. 1-4.
2. T.B. Smith, "Electricity theft- comparative analysis," Energy Policy, vol. 32, pp. 2067-2076, Aug. 2003.
3. A.J. Dick, "Theft of Electricity - how UK electricity companies detect and deter," Proc. European Convention Security and Detection, Brighton, U.K., May 1995, pp. 90-95.
4. S.S. Depuru, L. Wang, V. Devabhaktuni, and N. Gudi, "Measures and setbacks for controlling electricity theft," Proc. IEEE North American Power Symposium, Arlington, Texas, Sep. 2010.
5. "Pilferage of electricity - issues and challenges," power sector news, KSEB Officers' Association, [Online]. Available: http:// www.kseboa. org/news/pilferage-of-electricity-issues-and-challenges.html
6. R. Cespedes, H. Duran, H. Hernandez, and A. Rodriguez, "Assessment of electrical energy losses in the colombian power system," IEEE Trans. on Power Apparatus and Systems, vol. 102, pp. 3509-3515, Nov. 1983.
7. "Overview of power distribution," Ministry of Power, Govt. of India, [Online]. Available: http://www.powermin.nic.in
8. A. Pyasi and V. Verma, "Improvement in electricity distribution efficiency to mitigate pollution," Proc. IEEE International Symposium on Electronics and the Environment, San Francisco, California, May 2008, pp. 1-1.
9. S.S.S.R. Depuru, L. Wang, and V. Devabhaktuni, "Support vector machine based data classification for detection of electricity theft," 2011 IEEE PES Power Systems Conference & Expo, Phoenix, Arizona, Mar. 2011, submitted.
10. M. Begovic, D. Novosel, B. Milosevic, and M. Kostic, "Impact of distribution efficiency on generation and voltage stability," Proc. 33rd Annual Hawaii International Conference on System Sciences, Maui, Hawaii, Jan. 2000, pp. 7.
11. E.G. Buda, "System for accurately detecting electricity theft," U.S. Patent 12/351,978, Jan. 12, 2009.
12. C.J. Bandim, J.E.R. Alves, A.V. Pinto, F.C. Souza, M.R.B. Loureiro, C.A. Magalhaes, and D.F. Galvez, "Identification of energy theft and tampered meters using a central observer meter: a mathematical approach," Proc. IEEE PES Transmission and Distribution Conference and Exposition, Rio de Janeiro, Brazil, Sep. 2003, pp. 163-168.
13. R.D. Anand and S.A. Naveen, "Design and development of vigilant energy metering system (VEMS) and its applications," Proc. Student Conference on Research and Development, New Delhi, India, Aug. 2003, pp. 15-18.
14. A. Pasdar and S.A. Mirzakuchaki, "A solution to remote detecting of illegal electricity usage based on smart metering," Proc. International Workshop on Soft Computing Applications, Oradea, Romania, Aug. 2007, pp. 163-167.
15. M. Jamil, F. Munir, A.A. Khan, and A. Mirza, "Telemetering & billing system for spatially distributed electrical power clients," Proc. E-Tech 2004, Karachi, Pakistan, July. 2004, pp. 35-40.
16. S. De, R. Anand, A. Naveen, and S. Moinuddin, "E-metering solution for checking energy thefts and streamlining revenue collection in India,"Proc. IEEE PES Transmission and Distribution Conference and Exposition, Dallas, Texas, Sep. 2003, pp. 654-658.
17. S. McLaughlin, D. Podkuiko, and P. McDaniel, "Energy theft in the advanced metering infrastructure" [Online]. Available: http://www .patrickmcdaniel.org/pubs/critis09.pdf
18. G. Rob, J. Saskia, and W. Rob, "Smart metering," Leonardo Energy, [Online] Available: http://www.leonardo-energy.org/webfm-send/435
19. S.S. Depuru, L. Wang, and V. Devabhaktuni, "A Conceptual Design Using Harmonics to Reduce Pilfering of Electricity," IEEE PES General Meeting, Minneapolis, Minnesota, July 2010.
20. R. Mano, R. Cespedes, and D. Maia, "Protecting revenue in the distribution industry: a new approach with the revenue assurance and audit process," Proc. IEEE PES Transmission and Distribution Conference and Exposition: Latin America, Kema, Brazil, Nov. 2004, pp. 218-223.
21. J.E. Cabral and E.M. Gontijo, "Fraud detection in electrical energy consumers using rough sets," IEEE International Conference on Systems, Man and Cybernetics, Campo Grande, Brazil, Oct. 2004, pp. 3625-3629.
22. J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, and A. M. Mohammad, "Detection of abnormalities and electricity theft using genetic support vector machines" Proc. IEEE Region 10 Conference TENCON, Hyderabad, India, Jan. 2009, pp. 1-6.
23. J. Nagi, K. S. Yap, S. K. Tiong, S. K. Ahmed, and A. M. Mohammad, "Nontechnical loss detection for metered customers in power utility using support vector machines," IEEE Transactions on power delivery, vol. 25, pp. 1162, Apr. 2010.
24. A.H. Nizar and Z.Y. Dong, "Identification and detection of electricity customer behavior irregularities," Proc. IEEE PES Power Systems Conference and Exposition, Seattle, Washington, Mar. 2009, pp. 1-10.
25. A.H. Nizar, Z.Y Dong, and Y Wang, "Power utility nontechnical loss analysis with extreme learning machine method," IEEE Transactions on Power Systems, Volume: 23, pp. 946-955, Aug. 2008.
26. A.H. Nizar, Z.Y Dong, J.H, Zhao, and P. Zhang, "A Data Mining Based NTL Analysis Method," IEEE Power Engineering Society General Meeting, Tampa, Florida, June 2007, pp. 24-28.
27. V. N. Vapnik, Statistical Learning Theory, New York: Wiley, 1998.
28. N. Cristianini and J. S. Taylor, An Introduction to Support Vector machines. Cambridge, MA: Cambridge Univ. Press, 2000.
29. G. Dror, R. Sorek, and S. Shamir, "Accurate identification of alternatively spliced exons using support vector machine,"Bioinformatics, vol. 21, pp. 897-901, Apr. 2005.
30. B. E. Boser, I. Guyon, and V. Vapnik, "A training algorithm for optimal margin classifiers," Proc. The Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, Pennsylvania, 1992, pp. 144-152.
31. C.C. Chang and C.J. Lin, "LIBSVM - A Library for Support Vector Machines" [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
32. G. Gowrishankar and P.S. Satyanarayana, "Neural network based traffic prediction for wireless data networks," International Journal of Computational Intelligence Systems (IJCIS), vol.1, pp. 379-389, Dec. 2008.
33. B. Yang, X. Su, and Y. Wang, "Back propagation based on selective attention for fast convergence of training neural network," Proc. Fifth World Congress on Intelligent Control and Automation, Hangzhou, China, June 2004, pp. 2009-2013.
34. M. Chen and Z. Yao, "Classification techniques of neural networks using improved genetic algorithms," Proc. IEEE International Conference on Genetic and Evolutionary Computing, Hubei, China, Sept. 2008, pp. 115-119.
35. nd ed.), Addison Wesley, 2005).
36. nd ed.), Prentice Hall, 1999.
37. G.D. Magoulas, M.N. Vrahatis, and G.S. Androulakis, "Improving the convergence of the back-propagation algorithm using learning rate adaptation methods," Neural Computation, vol. 11, pp. 1769-1796, Oct. 1999.
38. S. Chai and Y. Zhou, "A study on how to help back-propagation escape local minimum," Proc. Third International Conference on Natural Computation, Haikou, China, Aug. 2007, pp. 64-68.
39. T. Kamio, S. Tanaka, and M. Morisue, "Back-propagation algorithm for logic oriented neural networks," IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, July 2000, pp. 123-128.
40. S Forrest, "Genetic algorithms: principles of natural selection applied to computation," Science, vol. 261, pp. 872-878, Aug. 1993.