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

ABSTRACT

Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consumption data. In recent years, the field has rapidly expanded due to increased interest as national deployments of smart meters have begun in many countries. However, empirically comparing disaggregation algorithms is currently virtually impossible. This is due to the different data sets used, the lack of reference implementations of these algorithms and the variety of accuracy metrics employed. To address this challenge, we present the Non-intrusive Load Monitoring Toolkit (NILMTK); an open source toolkit designed specifically to enable the comparison of energy disaggregation algorithms in a reproducible manner. This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets. Our toolkit includes parsers for a range of existing data sets, a collection of preprocessing algorithms, a set of statistics for describing data sets, two reference benchmark disaggregation algorithms and a suite of accuracy metrics. We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.

References

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.

Index Terms

• Computing methodologies

  • Machine learning