Residential appliance identification and future usage prediction from smart meter

IECON Proceedings (Industrial Electronics Conference)

Volumn , Issue , 2013, Pages 4994-4999

  Basu, Kaustav ^a   Debusschere, Vincent ^a   Bacha, Seddik ^a  

^a LABORATOIRE D ELECTROTECHNIQUE DE GRENOBLE   (France)

Author keywords

Appliance usage prediction; Datamining; Energy Management; Multi label classifier; Non intrusive load monitoring; Smart Grids; Smart Homes

Indexed keywords

ELECTRICAL APPLIANCES; MULTI-LABEL; NONINTRUSIVE LOAD MONITORING; RESIDENTIAL HOMES; SERVICE REQUESTS; SMART GRID; SMART HOMES; WEATHER INFORMATION;

AGGREGATES; AUTOMATION; DATA MINING; ELECTRIC LOAD FORECASTING; ENERGY MANAGEMENT; ENERGY UTILIZATION; FORECASTING; HOUSING; INDUSTRIAL ELECTRONICS; INTELLIGENT BUILDINGS; SMART METERS; SMART POWER GRIDS;

EQUIPMENT;

EID: 84893629154     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IECON.2013.6699944     Document Type: Conference Paper

References (22)

1. C. Clastres, T. H. Pham, F. Wurtz, and S.Bacha, "Ancillary services and optimal household energy management with pv production," Energy, Elsevier, vol. 35, no. 1, pp. 55-64, January 2010.
2. G. Hart, "Nonintrusive appliance load monitoring," Proceedings of the IEEE, vol. 80, no. 12, pp. 1870-1891, dec 1992.
3. M. E. Berges, E. Goldman, H. S. Matthews, and L. Soibelman, "Enhancing electricity audits in residential buildings with nonintrusive load monitoring," Journal of Industrial Ecology, vol. 14, no. 5, pp. 844-858, 2010. [Online]. Available: http://dx.doi.org/10.1111/j. 1530-9290.2010.00280.x
4. W. K. Lee, G. S. K. Fung, H. Y. Lam, F. H. Y. Chan, and M. Lucente, "Exploration on load signatures," Electrical Engineering, no. 725, pp. 1-5, 2004. [Online]. Available: http: //citeseerx.ist.psu.edu/viewdoc/ downloaddoi=10.1.1.120.5328
5. M. B. Figueiredo, A. de Almeida, and B. Ribeiro, "An experimental study on electrical signature identification of non-intrusive load monitoring (nilm) systems," in Proceedings of the 10th international conference on Adaptive and natural computing algorithms-Volume Part II, ser. ICANNGA'11. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 31-40. [Online]. Available: http://dl.acm.org/citation.cfm?id= 1997005.1997010
6. G. Kalogridis, C. Efthymiou, S. Denic, T. Lewis, and R. Cepeda, "Privacy for smart meters: Towards undetectable appliance load signatures," in Smart Grid Communications (SmartGridComm), 2010 First IEEE International Conference on, oct. 2010, pp. 232-237.
7. A. Prudenzi, "A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel," in Power Engineering Society Winter Meeting, 2002. IEEE, vol. 2, 2002, pp. 941-946 vol.2.
8. B. J. Birt, G. R. Newsham, I. Beausoleil-Morrison, M. M. Armstrong, N. Saldanha, and I. H. Rowlands, "Disaggregating categories of electrical energy end-use from whole-house hourly data," Energy and Buildings, vol. 50, no. 0, pp. 93-102, 2012. [Online]. Available: http://www.sciencedirect.com/ science/article/pii/S0378778812001739
9. N. A. Kolter J.Z, Batra S, "Energy disaggregation via disciminative sparce coding," Advances in Neural Information Processing Systems, vol. 23, no. 7, pp. 1153-1161, 2010.
10. T. Ha Pham, C. Clastres, F. Wurtz, S. Bacha, and E. Zama, "Optimal household energy management and economic analysis: from sizing to operation scheduling," 2008. [Online]. Available: http://halshs.archives-ouvertes.fr/ halshs-00323581/en
11. K. Basu, M. Guillame-Bert, H. Joumaa, S. Ploix, and J. Crowley, "Predicting home service demands from appliance usage data," in International Conference on Information and Communication Technologies and Applications ICTA, 2011.
12. K. Basu, L. Hawarah, N. Arghira, H. Joumaa, and S. Ploix, "A prediction system for home appliance usage," Energy and Buildings, 2013.
13. K. Basu, V. Debusschere, and S. Bacha, "Appliance usage prediction using a time series based classification approach," in IECON 2012-38th Annual Conference on IEEE Industrial Electronics Society. IEEE, 2012, pp. 1217-1222.
14. N. Ding, Y. Besanger, F. Wurtz, G. Antoine, and P. Deschamps, "Time series method for short-term load forecasting using smart metering in distribution systems," in PowerTech, 2011 IEEE Trondheim, june 2011, pp. 1-6.
15. K. Basu, V. Debusschere, and S. Bacha, "Load identification from power recordings at meter panel in residential households," in Electrical Machines (ICEM), 2012 XXth International Conference on, sept. 2012, pp. 2098-2104.
16. S. Laxman and P. S. Sastry, "A survey of temporal data mining," Sadhana, vol. 31, no. 2, pp. 173-198, 2006.
17. M. W. Kadous, "Temporal classification: Extending the classification paradigm to multivariate time series," PhD thesis, University of New South Wales, 2002.
18. O. Parson, S. Ghosh, M. Weal, and A. Rogers, "Using hidden markov models for iterative non-intrusive appliance monitoring," in Neural Information Processing Systems workshop on Machine Learning for Sustainability, December 2011, event Dates: 17 December 2011. [Online]. Available: http://eprints.soton.ac.uk/272990
19. H. Kim, M. Marwah, M. F. Arlitt, G. Lyon, and J. Han, "Unsupervised disaggregation of low frequency power measurements." in SDM. SIAM / Omnipress, 2011, pp. 747-758. [Online]. Available: http://dblp.uni-trier.de/db/ conf/sdm/sdm2011.html#KimMALH11
20. H. Yoon, K. Yang, and C. Shahabi, "Feature subset selection and feature ranking for multivariate time series," Knowledge and Data Engineering, IEEE Transactions on, vol. 17, no. 9, pp. 1186-1198, sept. 2005.
21. G. Tsoumakas and Katakis, "Multi Label Classification: An Overview," International Journal of Data Warehousing and Mining, vol. 3, no. 3, pp. 1-13, 2007. [Online]. Available: http://mlkd.csd.auth.gr/publicationn details.asp?publicationID=219
22. J. Read, B. Pfahringer, G. Holmes, and E. Frank, "Classifier Chains for Multi-label Classification," 2009, pp. 254-269. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-04174-7n 17