Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring

IEEE Transactions on Smart Grid

Volumn 7, Issue 6, 2016, Pages 2575-2585

  Makonin, Stephen ^a   Popowich, Fred ^a   Bajic, Ivan V ^a   Gill, Bob ^b   Bartram, Lyn ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

^b BRITISH COLUMBIA INSTITUTE OF TECHNOLOGY   (Canada)

Author keywords

energy modeling; hidden Markov model; HMM; Load disaggregation; NILM; nonintrusive load monitoring; sparsity; sustainability; Viterbi algorithm

Indexed keywords

ELECTRIC LOAD MANAGEMENT; HIDDEN MARKOV MODELS; INFERENCE ENGINES; MARKOV PROCESSES; SMART METERS; SUSTAINABLE DEVELOPMENT;

DISAGGREGATION; ENERGY MODEL; NILM; NONINTRUSIVE LOAD MONITORING; SPARSITY;

VITERBI ALGORITHM;

EID: 85027063617     PISSN: 19493053     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSG.2015.2494592     Document Type: Article

References (22)

1. G. W. Hart, "Nonintrusive appliance load monitoring," Proc. IEEE, vol. 80, no. 12, pp. 1870-1891, Dec. 1992.
2. S. Makonin et al., "A consumer bill of rights for energy conservation," in Proc. IEEE Canada Int. Humanitar. Technol. Conf. (IHTC), Montreal, QC, Canada, 2014, pp. 1-6.
3. M. Zeifman and K. Roth, "Viterbi algorithm with sparse transitions (VAST) for nonintrusive load monitoring," in Proc. IEEE Symp. Comput. Intell. Appl. Smart Grid (CIASG), Paris, France, 2011, pp. 1-8.
4. S. Makonin, I. V. Bajic, and F. Popowich, "Efficient sparse matrix processing for nonintrusive load monitoring (NILM)," in Proc. Int. Workshop Non-Intrus. Load Monitor., Austin, TX, USA, 2014, pp. 1-4.
5. S. Makonin, "Real-time embedded low-frequency load disaggregation," Ph.D. dissertation, School Comput. Sci., Simon Fraser Univ., Burnaby, BC, Canada, Aug. 2014.
6. M.-S. Tsai and Y.-H. Lin, "Modern development of an adaptive non-intrusive appliance load monitoring system in electricity energy conservation," Appl. Energy, vol. 96, pp. 55-73, Aug. 2012.
7. M. Figueiredo, A. de Almeida, and B. Ribeiro, "Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems," Neurocomputing, vol. 96, pp. 66-73, Nov. 2012.
8. M. E. Berges, E. Goldman, H. S. Matthews, and L. Soibelman, "Enhancing electricity audits in residential buildings with nonintrusive load monitoring," J. Ind. Ecol., vol. 14, no. 5, pp. 844-858, 2010.
9. C. M. Bishop and N. M. Nasrabadi, Pattern Recognition and Machine Learning, vol. 1. New York, NY, USA: Springer, 2006.
10. J. Z. Kolter and T. Jaakkola, "Approximate inference in additive factorial HMMs with application to energy disaggregation," J. Mach. Learn. Res. Proc. Track, vol. 22, pp. 1472-1482, Apr. 2012.
11. H. Kim, M. Marwah, M. Arlitt, G. Lyon, and J. Han, "Unsupervised disaggregation of low frequency power measurements," in Proc. SIAM Conf. Data Mining, Mesa, AZ, USA, 2011, pp. 747-758.
12. S. Makonin, F. Popowich, L. Bartram, B. Gill, and I. V. Bajic, "AMPds: A public dataset for load disaggregation and eco-feedback research," in Proc. IEEE Elect. Power Energy Conf. (EPEC), Halifax, NS, Canada, 2013, pp. 1-6.
13. T. Zia, D. Bruckner, and A. Zaidi, "A hidden Markov model based procedure for identifying household electric loads," in Proc. 37th Annu. Conf. IEEE Ind. Electron. Soc. (IECON), Melbourne, VIC, Australia, 2011, pp. 3218-3223.
14. M. Zeifman, "Disaggregation of home energy display data using probabilistic approach," IEEE Trans. Consum. Electron., vol. 58, no. 1, pp. 23-31, Feb. 2012.
15. O. Parson, "Unsupervised training methods for non-intrusive appliance load monitoring from smart meter data," Ph.D. dissertation, Dept. Electron. Comput. Sci., Univ. Southampton, Southampton, U.K., 2014.
16. M. J. Johnson and A. S. Willsky, "Bayesian nonparametric hidden semi- Markov models," J. Mach. Learn. Res., vol. 14, no. 1, pp. 673-701, 2013.
17. I. S. Duff, R. G. Grimes, and J. G. Lewis, "Sparse matrix test problems," ACM Trans. Math. Softw. (TOMS), vol. 15, no. 1, pp. 1-14, 1989.
18. J. Z. Kolter and M. J. Johnson, "REDD: A public data set for energy disaggregation research," in Proc. Workshop Data Mining Appl. Sustain. (SIGKDD), San Diego, CA, USA, 2011, pp. 1-6.
19. S. Makonin and F. Popowich, "Nonintrusive load monitoring (NILM) performance evaluation," Energy Efficien., vol. 8, no. 4, pp. 809-814, 2015.
20. B. Roberts, "Shaving load peaks from the substation," POWER Mag., pp. 1-2, Oct. 2006, [Online]. Available: www.powermag.com
21. J. Z. Kolter, S. Batra, and A. Y. Ng, "Energy disaggregation via discriminative sparse coding," in Proc. 24th Annu. Conf. Neural Inf. Proc. Syst. (NIPS), Vancouver, BC, Canada, 2010, pp. 1153-1161.
22. J. Kelly et al., "NILMTK v0.2: A non-intrusive load monitoring toolkit for large scale data sets: Demo abstract," in Proc. 1st ACM Conf. Embedded Syst. Energy Efficien. Build. (BuildSys), Memphis, TN, USA, 2014, pp. 182-183.