An approach of household power appliance monitoring based on machine learning

Proceedings - 2012 5th International Conference on Intelligent Computation Technology and Automation, ICICTA 2012

Volumn , Issue , 2012, Pages 577-580

  Jiang, Lei ^a   Luo, Suhuai ^a   Li, Jiaming ^b  

^a UNIVERSITY OF NEWCASTLE   (Australia)

^b CSIRO   (Australia)

Author keywords

NILM; Power feature; Residential appliances recognition; Smart grid; Support vector machine

Indexed keywords

NILM; POWER FEATURE; RESIDENTIAL APPLIANCES RECOGNITION; SMART GRID; SUPPORT VECTOR MACHINE (SVM);

SUPPORT VECTOR MACHINES;

EQUIPMENT;

EID: 84863240238     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICICTA.2012.151     Document Type: Conference Paper

References (25)

1. Y. Guo, et al., "Evolutionary Optimisation of Distributed Energy Resources" Proceedings, 18th Australian Joint Conference on Artificial Intelligence - AI 2005: Advances in Artificial Intelligence, Sydney, Australia, Dec 2005, vol. 3809, pp. 1086-1091.
2. R. Li, et al., "Agent-Based Optimisation Systems for Electrical Load Management" Proc. First International Workshop on Optimisation in Multi-Agent Systems, Estoril, Portugual, May 2008, pp 60-69.
3. J. Li, G. Poulton and G. James, et al., "Performance of Multi-agent Coordination of Distributed Energy Resources" WSEAS Transactions on System and Control, January 2007, Issue 1, Vol. 2, pp 52-58.
4. J. Li, G. Poulton and G. James, "Agent-Based Distributed Energy Management" 20th Australian Joint Conference on Artificial Intelligence- AI 2007: Advances in Artificial Intelligence, Dec. 2007, Australia, Volume: 4830, pp569-578.
5. J. Li, G. Poulton and G. James, "Coordination of Distributed Energy Resource Agents", Journal of Applied Artificial Intelligence, Volume 24, Issue 5, May 2010, pages 351 - 380.
6. J. Li, G. James and G. Poulton, "Set-Points Based Optimal Multi-Agent Coordination for Controlling Distributed Energy Loads" 3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems, San Francisco, California, September 14-18, 2009, pp265-271.
7. S. Inagaki, T. Egami, T. Suzuki, H. Nakamura and K. Ito, "Nonintrusive Appliance Load Monitoring Based on Integer Programming", Electrical Engineering in Japan, Vol. 174, No. 2, 2011 Translated from Denki Gakkai Ronbunshi, Vol. 128-B, No. 11, pp. 1386-1392, 2008.
8. Aoki S., Onishi M., Kojima A., Fukunaga K., "Detection of a solitude senior's irregular states based on learning and recognizing of behavioral patterns". IEEJ, 125:259-265, 2005.
9. S.B. Leeb, S.R. Shaw, and J.L. Kirley, "Transient Event Detection in Spectral Envelope Estimates for Nonintrusive Load Monitoring", IEEE Transactions on Power Delivery, Vol. 10, Cambridge, pp. 1200-1211, 1995.
10. G. W. Hart, "Nonintrusive Appliance Load Monitoring", Proceedings of the IEEE, Vol. 80, pp. 1870-1891, 1992.
11. M. C. Ferris and T. S. Munson, "Interior point methods for massive support vector machines", Technical Report 00-05, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin, 2000.
12. C. J. C. Burges. "A tutorial on support vector machines for pattern recognition." Data Mining and Knowledge Discovery, Vol.2, No.2, pp.121-167, 1998.
13. J. J. Moré and G. Toraldo. "Algorithms for bound constrained quadratic programs", Numerische Mathematik, Vol.55, pp.377-400, 1989.
14. O. L. Mangasarian, David R. Musicant, "Active Support Vector Machine Classification", Advances in Neural Information Processing Systems 13, 2001.
15. O. L. Mangasarian. "Nonlinear Programming. SIAM", Philadelphia, PA, 1994.
16. T. Onodat, H. Muratat, G. Ratscht and K. R. Mulled, C. CRIEPI, Iwadokita, Komae-shi, "Experimental Analysis of Support Vector Machines with Different Kernels based on Non-Intrusive Monitoring Data", IEEE, 0-7803-7278-6/02, pp. 2186 - 2191, 2002.
17. R. Kadouche, B. Chikhaoui, B. Abdulrazak, "User's behavior study for smart houses occupant prediction", Ann. Telecommun. Vol.65, pp.539-543, 2010.
18. Guillermo L. Grinblat, Lucas C. Uzal, H. Alejandro Ceccatto, and Pablo M. Granitto, "Solving Non-stationary Classification Problems with Coupled Support Vector Machines", IEEE Transaction On Neural Networks, Vol. 22, No. 1, 2011.
19. Joachims T., "Making Large-Scale SVM Learning Practical". LS8-Report, University of Dortmund, LS VIII-Report, 1998.
20. Ai-bing Ji, Jia-hong Pang, Hong-jie Qiu, "Support vector machine for classification based on fuzzy training data", Expert Systems with Applications 37, pp.3495-3498, 2010.
21. S. Luo, Q. Hu, X. He, J. Li, S. J. Jin, M. Park, "Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines", IEEE/CME International Conference on Complex Medical Engineering, Tempe, USA, paper 10071, 2009.
22. M. Zeifman, K. Roth, "Nonintrusive Appliance Load Monitoring: Review and Outlook", IEEE Transactions on Consumer Electronics, Vol. 57, No.1, 2011.
23. Debnath R., Takahashi, "Kernel selection for the support vector machine (biocybernetics, neurocomputing)", IEICE Trans Inf Syst, Vol.E87D, No.12, pp:2903-2934, 2004.
24. S. R. Gunn, "Support Vector Machines for Classification and Regression", University of Southampton, Technical Report, Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science, 1998.
25. J. Li, S Luo and J. S. Jin, "Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory", Sensors 2010, 10(10), pp. 9384-9396.