NILMTK: An open source toolkit for non-intrusive load monitoring

e-Energy 2014 - Proceedings of the 5th ACM International Conference on Future Energy Systems

Volumn , Issue , 2014, Pages 265-276

  Batra, Nipun ^a   Kelly, Jack ^b   Parson, Oliver ^c   Dutta, Haimonti ^d   Knottenbelt, William ^b   Rogers, Alex ^c   Singh, Amarjeet ^a   Srivastava, Mani ^e  

^a INDRAPRASTHA INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^d CCLS Columbia ^*  (United States)

^e UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

energy disaggregation; non intrusive load monitoring; smart meters

Indexed keywords

EID: 84907017716     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2602044.2602051     Document Type: Conference Paper

References (33)

1. G. W. Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12):1870-1891, 1992. doi:10.1109/5.192069.
2. S. Darby. The effectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays, 2006.
3. California Public Utilities Commission. Final Opinion Authorizing Pacific Gas and Electric Company to Deploy Advanced Metering Infrastructure. Technical report, 2006.
4. Department of Energy & Climate Change. Smart Metering Equipment Technical Specifications Version 2. Technical report, UK, 2013.
5. J. Z. Kolter and M. J. Johnson. REDD: A public data set for energy disaggregation research. In Proceedings of 1st KDD Workshop on Data Mining Applications in Sustainability, San Diego, CA, USA, 2011.
6. K. Anderson, A. Ocneanu, D. Benitez, D. Carlson, A. Rowe, and M. Bergés. BLUED: A fully labeled public dataset for Event-Based Non-Intrusive load monitoring research. In Proceedings of 2nd KDD Workshop on Data Mining Applications in Sustainability, pages 12-16, Beijing, China, 2012.
7. S. Barker, A. Mishra, D. Irwin, E. Cecchet, P. Shenoy, and J. Albrecht. Smart*: An open data set and tools for enabling research in sustainable homes. In Proceedings of 2nd KDD Workshop on Data Mining Applications in Sustainability, Beijing, China, 2012.
8. A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220, 2000. doi:10.1161/01.cir.101.23.e215.
9. D. Kotz and T. Henderson. Crawdad: A community resource for archiving wireless data at dartmouth. Pervasive Computing, IEEE, 4(4):12-14, 2005. doi:10.1109/MPRV.2005.75.
10. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011. arXiv:1201.0490.
11. H. Kim, M. Marwah, M. F. Arlitt, G. Lyon, and J. Han. Unsupervised Disaggregation of Low Frequency Power Measurements. In Proceedings of 11th SIAM International Conference on Data Mining, pages 747-758, Mesa, AZ, USA, 2011. doi:10.1137/1.9781611972818.64.
12. K. C. Armel, A. Gupta, G. Shrimali, and A. Albert. Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy, 52:213-234, 2013. doi:10.1016/j.enpol.2012.08.062.
13. C. Holcomb. Pecan Street Inc.: A Test-bed for NILM. In International Workshop on Non-Intrusive Load Monitoring, Pittsburgh, PA, USA, 2012.
14. 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. Technical Report R66141, DEFRA, May 2012.
15. S. Makonin, F. Popowich, L. Bartram, B. Gill, and I. V. Bajic. AMPds: A Public Dataset for Load Disaggregation and Eco-Feedback Research. In IEEE Electrical Power and Energy Conference, Halifax, NS, Canada, 2013.
16. N. Batra, M. Gulati, A. Singh, and M. B. Srivastava. It's Different: Insights into home energy consumption in India. In Proceedings of the Fifth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, 2013. doi:10.1145/2528282.2528293.
17. J. Kelly and W. Knottenbelt. UK-DALE: A dataset recording UK Domestic Appliance-Level Electricity demand and whole-house demand. ArXiv e-prints, 2014. arXiv:1404.0284.
18. J. Z. Kolter and T. Jaakkola. Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation. In Proceedings of the International Conference on Artificial Intelligence and Statistics, pages 1472-1482, La Palma, Canary Islands, 2012.
19. M. Zeifman. Disaggregation of home energy display data using probabilistic approach. IEEE Transactions on Consumer Electronics, 58(1):23-31, 2012. doi:10.1109/TCE.2012.6170051.
20. M. J. Johnson and A. S. Willsky. Bayesian Nonparametric Hidden Semi-Markov Models. Journal of Machine Learning Research, 14:673-701, 2013. arXiv:1203.1365.
21. O. Parson, S. Ghosh, M. Weal, and A. Rogers. Non-intrusive load monitoring using prior models of general appliance types. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, pages 356-362, Toronto, ON, Canada, 2012.
22. D. Rahayu, B. Narayanaswamy, S. Krishnaswamy, C. Labbe, and D. P. Seetharam. Learning to be energy-wise: Discriminative methods for load disaggregation. In 3rd International Conference on Future Energy Systems, pages 1-4, 2012. doi:10.1145/2208828.2208838.
23. N. Batra, H. Dutta, and A. Singh. INDiC: Improved Non-Intrusive load monitoring using load Division and Calibration. In International Conference of Machine Learning and Applications, Miami, FL, USA, 2013.
24. K. Anderson, M. Berges, A. Ocneanu, D. Benitez, and J. Moura. Event detection for non intrusive load monitoring. In Proceedings of 38th Annual Conference on IEEE Industrial Electronics Society, pages 3312-3317, 2012. doi:10.1109/IECON.2012.6389367.
25. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Graphlab: A new parallel framework for machine learning. In Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, 2010. arXiv:1006.4990.
26. A. Schoofs, A. Guerrieri, D. T. Delaney, G. O'Hare, and A. G. Ruzzelli. ANNOT: Automated Electricity Data Annotation Using Wireless Sensor Networks. In Proceedings of the 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks, Boston, MA, USA, 2010. doi:10.1109/SECON.2010.5508248.
27. J. Kelly and W. Knottenbelt. Metadata for Energy Disaggregation. ArXiv e-prints, Mar. 2014. arXiv:1403.5946.
28. Z. Ghahramani and M. I. Jordan. Factorial hidden markov models. Machine learning, 29(2-3):245-273, 1997. doi:10.1023/A:1007425814087. (Pubitemid 127510040)
29. Richard de Dear and Melissa Hart. Appliance Electricity End-Use: Weather and Climate Sensitivity. Technical report, Sustainable Energy Group, Australian Greenhouse Office, 2002.
30. A. Kavousian, R. Rajagopal, and M. Fischer. Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior. Energy, 55(0):184-194, 2013. doi:10.1016/j.energy.2013.03.086.
31. M. Wytock and J. Zico Kolter. Contextually Supervised Source Separation with Application to Energy Disaggregation. ArXiv e-prints, 2013. arXiv:1312.5023.
32. S. Barker, S. Kalra, D. Irwin, and P. Shenoy. Empirical characterization and modeling of electrical loads in smart homes. In IEEE International Green Computing Conference, pages 1-10, Arlington, VA, USA, 2013. doi:10.1109/IGCC. 2013.6604512.
33. J. Liang, S. K. K. Ng, G. Kendall, and J. W. M. Cheng. Load Signature Study - Part II: Disaggregation Framework, Simulation, and Applications. IEEE Transactions on Power Delivery, 25(2):561-569, 2010. doi:10.1109/TPWRD.2009. 2033800.