Sustainable energy consumption monitoring in residential settings

Proceedings - IEEE INFOCOM

Volumn , Issue , 2013, Pages 3177-3182

  Akshay Uttama Nambi, S N ^a   Papaioannou, Thanasis G ^a   Chakraborty, Dipanjan ^b   Aberer, Karl ^a  

^a EPFL   (Switzerland)

^b IBM RESEARCH   (United States)

Author keywords

Energy disaggregation; FHMM; Hidden Markov Models; NILM; plug level meter

Indexed keywords

DEMAND RESPONSE; DISAGGREGATION; FHMM; HEURISTIC APPROACH; MINIMUM SUBSETS; MONITORING ACCURACY; NILM; SUSTAINABLE ENERGY;

ENERGY UTILIZATION; HEURISTIC METHODS; HIDDEN MARKOV MODELS;

HOUSING;

EID: 84883085720     PISSN: 0743166X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/INFCOM.2013.6567134     Document Type: Conference Paper

References (15)

1. M. Zeifman and K. Roth. Nonintrusive Appliance Load Monitoring (NIALM) for Energy Control in Residential Buildings: Review and Outlook. IEEE Transactions on Consumer Electronics, vol. 57, 2011.
2. G. Hart. NonIntrusive Appliance Load Monitoring. In Proc. of the IEEE, 80(2):1870-1891, 1992.
3. J. Paradiso, P. Dutta, H. Gellersen, E. Schooler. Smart Energy Systems. In Proc. of IEEE Pervasive Computing, 2011.
4. A.G. Ruzzelli, C. Nicolas, A. Schoofs, G.M.P. O'Hare. Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor. In Proc. of SECON, 2010.
5. S. Patel, T. Robertson, J. Kientz, M. Reynolds, G. Abowd. At the flick of a switch: detecting and classifying unique electrical events on the residential power line. In Proc. of UbiComp, 2007.
6. J. Zico Kolter and Matthew J. Johnson. REDD: A public data set for energy disaggregation research. In Proc. of the SustKDD Workshop on Data Mining Applications in Sustainability, 2011.
7. J. Z. Kolter, S. Batra, A. Y. Ng. Energy Disaggregation via Discriminative Sparse Coding. In Proc. of Neural Information Processing Systems, 2010.
8. L. Farinaccio, R. Zmeureanu. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy and Buildings, 30:245-259, 1999.
9. Y. Kim, T. Schmid, Z. M. Charbiwala, M. B. Srivastava. ViridiScope: design and implementation of a fine grained power monitoring system for homes. In Proc. of UbiComp, 2009.
10. T. Saitoh et al. Current Sensor based Non-intrusive Appliance Recognition for Intelligent Outlet In Proc. of the Int. Tech Conf on Circuits/Systems, Computers and Communications, 2008.
11. O. Parson , S. Ghosh, M. Weal, A. Rogers Non-intrusive Load Monitoring using Prior Models of General Appliance Types. In Proc. of AAAI, 2012.
12. Kim, H., Marwah, M., Arlitt, M.F., Lyon, G., Han, J. Unsupervised Disaggregation of Low Frequency Power Measurements. In Proc. of SIAM International Conference on Data Mining, 2011.
13. O. Ardakanian, S. Keshav, and C. Rosenberg. Markovian models for home electricity consumption. In Proc. of the ACM SIGCOMM Workshop on Green Networking (GreenNets '11), 2011.
14. T. Kato, H. S. Cho, D. Lee, T. Toyomura and T. Yamazaki. Appliance Recognition from Electric Current Signals for Information-Energy Integrated Network in Home Environments. In Proc. of ICOST, 2009.
15. G. Zoubin, J. Michael. Factorial Hidden Markov Models. Machine Learning, 29(2-3):245-273, 1997.