Profiling household appliance electricity usage with N-gram language modeling

Proceedings of the IEEE International Conference on Industrial Technology

Volumn 2016-May, Issue , 2016, Pages 604-609

  Li, Daoyuan ^a   Bissyande, Tegawende F ^a   Kubler, Sylvain ^a   Klein, Jacques ^a   Le Traon, Yves ^a  

^a UNIVERSITY OF LUXEMBOURG   (Luxembourg)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; CONSUMER BEHAVIOR; DOMESTIC APPLIANCES; ELECTRIC POWER UTILIZATION; ENERGY CONSERVATION; ENERGY UTILIZATION; SENSORY PERCEPTION;

CLASSIFICATION ACCURACY; DISCRIMINATIVE FEATURES; ELECTRICITY USAGE; ENERGY READINGS; LANGUAGE MODEL; N-GRAM LANGUAGE MODELS; PUBLIC DATASET; STATE-OF-THE-ART APPROACH;

MODELING LANGUAGES;

EID: 84974653215     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIT.2016.7474818     Document Type: Conference Paper

References (23)

1. J. Morales Pedraza, Electrical Energy Generation in Europe: The Current Situation and Perspectives in the Use of Renewable Energy Sources and Nuclear Power for Regional Electricity Generation, Springer, Ed. Springer, 2015.
2. S. Darby, "Making it obvious: designing feedback into energy consumption, " in Energy efficiency in household appliances and lighting. Springer, 2001, pp. 685-696.
3. G. W. Hart, "Nonintrusive appliance load monitoring, " Proceedings of the IEEE, vol. 80, no. 12, pp. 1870-1891, 1992.
4. A. Barbato, A. Capone, M. Rodolfi, and D. Tagliaferri, "Forecasting the usage of household appliances through power meter sensors for demand management in the smart grid, " in IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2011, pp. 404-409.
5. J. Lines, A. Bagnall, P. Caiger-Smith, and S. Anderson, "Classification of household devices by electricity usage profiles, " in Intelligent Data Engineering and Automated Learning-IDEAL 2011. Springer, 2011, pp. 403-412.
6. E. Elhamifar and S. Sastry, "Energy disaggregation via learning powerlets and sparse coding, " in Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
7. M. Zeifman and K. Roth, "Nonintrusive appliance load monitoring: Review and outlook, " IEEE Transactions on Consumer Electronics, pp. 76-84, 2011.
8. 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.
9. C. Laughman, K. Lee, R. Cox, S. Shaw, S. Leeb, L. Norford, and P. Armstrong, "Power signature analysis, " Power and Energy Magazine, IEEE, vol. 1, no. 2, pp. 56-63, 2003.
10. K. Basu, V. Debusschere, and S. Bacha, "Appliance usage prediction using a time series based classification approach, " in Thirty-Eighth Annual Conference of the IEEE Industrial Electronics Society (IECON). IEEE, 2012, pp. 1217-1222.
11. A. Nanopoulos, R. Alcock, and Y. Manolopoulos, "Feature-based classification of time-series data, " International Journal of Computer Research, vol. 10, no. 3, 2001.
12. J. J. Rodrguez and C. J. Alonso, "Interval and dynamic time warpingbased decision trees, " in Proceedings of the 2004 ACM symposium on Applied computing. ACM, 2004, pp. 548-552.
13. Y. Wu and E. Y. Chang, "Distance-function design and fusion for sequence data, " in Proceedings of the thirteenth ACM international conference on Information and knowledge management. ACM, 2004, pp. 324-333.
14. G. E. Batista, X. Wang, and E. J. Keogh, "A complexity-invariant distance measure for time series. " in Proceedings of the Eleventh SIAM International Conference on Data Mining (SDM), vol. 11, 2011, pp. 699-710.
15. C. A. Ratanamahatana and E. Keogh, "Three myths about dynamic time warping data mining, " in Proceedings of the 2005 SIAM International Conference on Data Mining (SDM05), 2005, pp. 506-510.
16. L. Ye and E. Keogh, "Time series shapelets: A new primitive for data mining, " in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009, pp. 947-956.
17. A. Mueen, E. Keogh, and N. Young, "Logical-shapelets: An expressive primitive for time series classification, " in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011, pp. 1154-1162.
18. T. Rakthanmanon and E. Keogh, "Fast shapelets: A scalable algorithm for discovering time series shapelets, " in Proceedings of the thirteenth SIAM conference on data mining (SDM), 2013.
19. J. Lin, E. Keogh, L. Wei, and S. Lonardi, "Experiencing sax: A novel symbolic representation of time series, " Data Mining and knowledge discovery, vol. 15, no. 2, pp. 107-144, 2007.
20. J. M. Ponte and W. B. Croft, "A language modeling approach to information retrieval, " in Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, 1998, pp. 275-281.
21. S. Varrette, P. Bouvry, H. Cartiaux, and F. Georgatos, "Management of an academic hpc cluster: The ul experience, " in Proc. of the 2014 Intl. Conf. on High Performance Computing & Simulation (HPCS 2014). Bologna, Italy: IEEE, July 2014, pp. 959-967.
22. Y. Chen, E. Keogh, B. Hu, N. Begum, A. Bagnall, A. Mueen, and G. Batista, "The ucr time series classification archive, " July 2015, www. cs. ucr. edu/?eamonn/time series data/.
23. J.-P. Zimmermann, M. Evans, J. Griggs, N. King, L. Harding, P. Roberts, and C. Evans, "Household electricity survey: A study of domestic electrical product usage, " Intertek Testing & Certification Ltd, 2012.