A temporal motif mining approach to unsupervised energy disaggregation: Applications to residential and commercial buildings

Proceedings of the 27th AAAI Conference on Artificial Intelligence, AAAI 2013

Volumn , Issue , 2013, Pages 1327-1333

  Shao, Huijuan ^a,b   Marwah, Manish ^c   Ramakrishnan, Naren ^a,b  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b State University   (United States)

^c HEWLETT PACKARD LABORATORIES   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMMERCIAL BUILDING; COMPUTATIONAL PROBLEM; CONSUMPTION PATTERNS; DISAGGREGATION; INDIVIDUAL DEVICES; NON-INTRUSIVE APPLIANCE LOAD MONITORING; PROCESSING STEPS; TEMPORAL ORDER;

ARTIFICIAL INTELLIGENCE; HOUSING; OFFICE BUILDINGS;

ENERGY UTILIZATION;

EID: 84893384215     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Agrawal, R., and Srikant, R. 1995. Mining sequential patterns. In Data Engineering, 1995. Proceedings of the Eleventh International Conference on, 3-14. IEEE.
2. Chan, W.; So, A.; and Lai, L. 2000. Harmonics load signature recognition by wavelets transforms. In Electric Utility Deregulation and Restructuring and Power Technologies, 2000. Proceedings. DRPT 2000. International Conference on, 666-671. IEEE.
3. Chiu, B.; Keogh, E.; and Lonardi, S. 2003. Probabilistic Discovery of Time Series Motifs. In the Ninth ACM International Conference on Knowledge Discovery and Data Mining, 493-498.
4. Görür, D., and Rasmussen, C. 2010. Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution. Journal of Computer Science and Technology 25(4):653-664.
5. Hart, G. 1992. Nonintrusive appliance load monitoring. Proceedings of the IEEE 80(2):1870-1891.
6. Kim, H.; Marwah, M.; Arlitt, M.; Lyon, G.; and Han, J. 2010. Unsupervised disaggregation of low frequency power measurements. In 11th International Conference on Data Mining, 747-758.
7. Kolter, J., and Jaakkola, T. 2012. Approximate inference in additive factorial hmms with application to energy disaggregation. In Journal of Machine Learning Research-Proceedings Track (JMLR), volume 22, 1472-1482.
8. Kolter, J., and Johnson, M. 2011. Redd: A public data set for energy disaggregation research. In Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA.
9. Kolter, J.; Batra, S.; and Ng, A. 2010. Energy disaggregation via discriminative sparse coding. In Proc. Neural Information Processing Systems.
10. Lam, H.; Fung, G.; and Lee, W. 2007. A novel method to construct taxonomy electrical appliances based on load signatures. Consumer Electronics, IEEE Transactions on 53(2):653-660.
11. Laxman, S.; Tankasali, V.; and White, R. 2008. Stream Prediction using a Generative Model based on Frequent Episodes in Event Sequences. In the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 453-461.
12. Nakano, Y., and Murata, H. 2007. Non-intrusive electric appliances load monitoring system using harmonic pattern recognition-trial application to commercial building. In Int. Conf. Electrical Engineering, Hong Kong, China.
13. Norford, L., and Leeb, S. 1996. Non-intrusive electrical load monitoring in commercial buildings based on steadystate and transient load-detection algorithms. Energy and Buildings 24(1):51-64.
14. Parson, O.; Ghosh, S.;Weal, M.; and Rogers, A. 2012. Nonintrusive load monitoring using prior models of general appliance types. In Proceedings of the 26th AAAI Conference on Artificial Intelligence.
15. Patel, S.; Robertson, T.; Kientz, J.; Reynolds, M.; and Abowd, G. 2007. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In Proceedings of the 9th international conference on Ubiquitous computing, 271-288. Springer-Verlag.
16. Patnaik, D.; Marwah, M.; Sharma, R.; and Ramakrishnan, N. 2009. Sustainable Operation and Management of Data Center Chillers using Temporal Data Mining. In the 15th ACM International Conference on Knowledge Discovery and Data Mining, 1305-1314.
17. Shaw, S. 2000. System Identification Techniques and Modeling for Nonintrusive Load Diagnositics. Ph.D. Dissertation, Massachusetts Institute of Technology, Boston, MA.
18. Srinivasan, D.; Ng, W.; and Liew, A. 2006. Neural-networkbased signature recognition for harmonic source identification. Power Delivery, IEEE Transactions on 21(1):398-405.
19. Yankov, D.; Keogh, E.; Medina, J.; Chiu, B.; and Zordan, V. 2007. Detecting Time Series Motifs under Uniform Scaling. In the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 844-853.