- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 742461, 10 pages
Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine
1School of DCIT, University of Newcastle, Callaghan, NSW 2308, Australia
2ICT Centre, Commonwealth Scientific and Industrial Research Organization, Clayton South, VIC 3169, Australia
3Energy Technology Division, Commonwealth Scientific and Industrial Research Organization, Clayton South, VIC 3169, Australia
Received 30 April 2012; Revised 8 August 2012; Accepted 18 October 2012
Academic Editor: F. Morabito
Copyright © 2012 Lei Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- G. Guo, S. Li, and K. Chan, “Face recognition by support vector machines,” in Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 196–201, 2000.
- S. Du and T. Wu, “Support Vector Machines for Regression,” Acta Simulata Systematica Sinica, TP18, CNKI:SUN:XTFZ.0.2003-11-022, 2003.
- 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, pp. 1386–1392, 2011.
- S. Aoki, M. Onishi, A. Kojima, and K. Fukunaga, “Detection of a solitude senior’s irregular states based on learning and recognizing of behavioral patterns,” IEEJ, vol. 125, pp. 259–265, 2005.
- C. Laughman, K. D. Lee, R. Cox et al., “Advanced nonintrusive monitoring of electric loads,” IEEE Power and Energy Magazine, pp. 56–563, April 2003.
- J. Li, G. Poulton, and G. James, “Agent-based distributed energy management,” in Proceedings of the 20th Australian Joint Conference on Advances in Artificial Intelligence, vol. 4830, pp. 569–578, December 2007.
- J. Li, G. Poulton, and G. James, “Coordination of distributed energy resource agents,” Applied Artificial Intelligence, vol. 24, no. 5, pp. 351–380, 2010.
- Y. Guo, J. Li, and G. James, “Evolutionary optimisation of distributed energy resources,” in Proceedings of the 18th Australian Joint Conference on Advances in Artificial Intelligence, vol. 3809, pp. 1086–1091, Sydney, Australia, December 2005.
- R. Li, J. Li, G. Poulton, and G. James, “Agent-based optimisation systems for electrical load management,” in Proceedings of the 1st International Workshop on Optimisation in Multi-Agent Systems, pp. 60–69, Estoril, Portugual, May 2008.
- J. Li, G. James, and G. Poulton, “Set-points based optimal multi-agent coordination for controlling distributed energy loads,” in Proceedings of the 3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO '09), pp. 265–271, San Francisco, Calif, USA, September 2009.
- J. Li, G. Poulton, G. James, and Y. Guo, “Multiple energy resource agent coordination based on electricity price,” Journal of Distributed Energy Resources, vol. 5, pp. 103–120, 2009.
- G. W. Hart, “Nonintrusive appliance load monitoring,” Proceedings of the IEEE, vol. 80, no. 12, pp. 1870–1891, 1992.
- S. B. Leeb, S. R. Shaw, and J. L. Kirtley, “Transient event detection in spectral envelope estimates for nonintrusive load monitoring,” IEEE Transactions on Power Delivery, vol. 10, no. 3, pp. 1200–1210, 1995.
- M. C. Ferris and T. S. Munson, “Interior point methods for massive support vector machines,” Tech. Rep. 00-05, Computer Sciences Department, University of Wisconsin, Madison, Wis, USA, 2000.
- 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.
- J. J. Moré and G. Toraldo, “Algorithms for bound constrained quadratic programming problems,” Numerische Mathematik, vol. 55, no. 4, pp. 377–400, 1989.
- O. L. Mangasarian and D. R. Musicant, “Active set support vector machine classifiation,” in Advances in Neural Information Processing Systems, T. Leen, T. Dietterich, and V. Tresp, Eds., vol. 13, pp. 577–583, MIT Press, Cambridge, Mass, USA, 2001.
- J. Li, S. West, and G. Platt, “Power decomposition based on SVM regression,” in Proceedings of the 4th International Conference on Modelling, Identification and Control (ICMIC '12), pp. 1256–1261, Wuhan, China, June 2012.
- A. J. Bijker, X. Xia, and J. Zhang, “Active power residential non-intrusive appliance load monitoring system,” in IEEE AFRICON Conference, pp. 1–6, September 2009.
- H.-H. Chang, K.-L. Chen, Y.-P. Tsai, and W.-J. Lee, “A new measurement method for power signatures of non-intrusive demand monitoring and load identification,” in Proceedings of the 46th IEEE Industry Applications Society Annual Meeting (IAS '11), pp. 1–7, Orlando, Fla, USA, 2011.
- T. Onoda, H. Murata, G. Rätsch, and K.-R. Müller, “Experimental analysis of Support Vector Machines with different kernels based on non-intrusive monitoring data,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '02), vol. 3, pp. 2186–2191, Honolulu, Hawaii, USA, 2002.
- R. Kadouche, B. Chikhaoui, and B. Abdulrazak, “User's behavior study for smart houses occupant prediction,” Annals of Telecommunications, vol. 65, no. 9-10, pp. 539–543, 2010.
- S. R. Gunn, “Support vector machines for classification and regression,” Faculty of Engineering, Science and Mathematics School of Electronics and Computer, University of Southampton, 1998.
- G. L. Grinblat, L. C. Uzal, H. A. Ceccatto, and P. M. Granitto, “Solving nonstationary classification problems with coupled upport vector machines,” IEEE Transactions on Neural Networks, vol. 22, no. 1, pp. 37–51, 2011.
- J. Liang, S. K. K. Ng, G. Kendall, and J. W. M. Cheng, “Load signature studypart I: basic concept, structure, and methodology,” IEEE Transactions on Power Delivery, vol. 25, no. 2, pp. 551–560, 2010.
- M. Zeifman and K. Roth, “Nonintrusive appliance load monitoring: review and outlook,” IEEE Transactions on Consumer Electronics, vol. 57, no. 1, pp. 76–84, 2011.
- J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986.
- D. Demigny and T. Kamlé, “A discrete expression of canny's criteria for step edge detector performances evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp. 1199–1211, 1997.
- F. Truchetet, F. Nicolier, and O. Laligant, “Subpixel edge detection for dimensional control by artificial vision,” Journal of Electronic Imaging, vol. 10, no. 1, pp. 234–239, 2001.
- I. Pitas and A. Venetsanopoulos, “Nonlinear mean filters in image processing,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, no. 3, pp. 573–584, 1986.
- A. Benazza-Benyahia, J. C. Pesquet, and H. Krim, “A nonlinear diffusion-based three-band filter bank,” IEEE Signal Processing Letters, vol. 10, no. 12, pp. 360–363, 2003.
- M. A. Schulze, “An edge-enhancing nonlinear filter for reducing multiplicative noise,” in Nonlinear Image Processing VIII, E. R. Dougherty and J. Astola, Eds., vol. 3026 of Proceedings of SPIE, pp. 46–56, San Jose, Calif, USA, February 1997.
- H. Hwang and R. A. Haddad, “Multilevel nonlinear filters for edge detection and noise suppression,” IEEE Transactions on Signal Processing, vol. 42, no. 2, pp. 249–258, 1994.
- O. Laligant and F. Truchetet, “A nonlinear derivative scheme applied to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 242–257, 2010.
- L. Jiang, S. Luo, and J. Li, “An approach of household power appliance monitoring based on machine learning,” in Proceedings of the 5th International Conference on Intelligent Computation Technology and Automation (ICICTA '12), pp. 577–580, January 2012.
- T. Joachims, “Making large-scale SVM learning practical,” LS8-Report, University of Dortmund, 1998.
- C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
- A. B. Ji, J. H. Pang, and H. J. Qiu, “Support vector machine for classification based on fuzzy training data,” Expert Systems with Applications, vol. 37, no. 4, pp. 3495–3498, 2010.
- S. Luo, Q. Hu, X. He, J. Li, J. S. Jin, and M. Park, “Automatic liver parenchyma segmentation from abdominal CT images using support vector machines,” in Proceedings of the IEEE/CME International Conference on Complex Medical Engineering (ICME '09), p. 10071, Tempe, Ariz, USA, April 2009.
- L. Jiang, J. Li, S. Luo, and S. West, “Literature review of power disaggregation,” in Proceedings of the IEEE International Conference on Modelling Identification and Control, pp. 38–42, 2011.
- R. Debnath and H. Takahashi, “Kernel selection for the support vector machine,” IEICE Transactions on Information and Systems, vol. E87-D, no. 12, pp. 2903–2904, 2004.