Frank Englert
ABSTRACT
Most current home automation systems are confined to a timer-based control of light and heating in order to improve the user's comfort. Additionally, these systems can be used to achieve energy savings, e.g., by turning the appliances off during the user's absence. The configuration of such systems, however, represents a major hindrance to their widespread deployment, as each connected appliance must be individually configured and assigned an operation schedule. The detection of active appliances as well as their current operating mode represents an enabling technology on the way to truly smart buildings. Once appliance identities are known, the devices can be deactivated to save energy or automatically controlled to increase the user's comfort.
In this paper, we propose an approach to have buildings informed about the presence and activity of electric appliances. It relies on distributed high-frequency measurements of electrical voltage and current and feature extraction process that distills the collected data into distinct features. We utilize a supervised machine learning algorithm to classify readings into the underlying device type as well as its operation mode, which achieves an accuracy of up to 99.8%.
- C. Beckel, L. Sadamori, and S. Santini. Towards Automatic Classification of Private Households Using Electricity Consumption Data. In 4th Intl. Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (BuildSys). {ACM}, 2012. Google Scholar
Digital Library
- H. Chang, C. Lin, and J. Lee. Load Identification in Nonintrusive Load Monitoring Using Steady-State and Turn-On Transient Energy Algorithms. In 14th Intl. Conference on Computer Supported Cooperative Work in Design (CSCWD), pages 27--32. {IEEE}, 2010.Google Scholar
- P. Emerald. Non Intrusive'Hall Effect Current Sensing Techniques Provide Safe, Reliable Detection and Protection for Power Electronics. In Intl. Appliance Technical Conference (IATC), pages 1--2. IEEE, 1998.Google Scholar
- T. Ganu, D. Seetharam, V. Arya, R. Kunnath, J. Hazra, S. Husain, L. De Silva, and S. Kalyanaraman. nPlug: A Smart Plug for Alleviating Peak Loads. In 3rd Intl. Conference on Future Energy Systems: Where Energy, Computing and Communication Meet (e-Energy), pages 1--10. ACM, 2012. Google ScholarDigital Library
- S. Gupta, M. S. Reynolds, and S. N. Patel. ElectriSense: Single-Point Sensing Using EMI for Electrical Event Detection and Classification in the Home. In 12th Intl. Conference on Ubiquitous Computing (UbiComp), pages 139--148. ACM, 2010. Google ScholarDigital Library
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA Data Mining Software: An Update. SIGKDD Explorations, 11(1):10--18, 2009. Google ScholarDigital Library
- G. Hart. Nonintrusive Appliance Load Monitoring. Proceedings of the IEEE, 80(12):1870--1891, 1992.Google ScholarCross Ref
- M. Ito, R. Uda, S. Ichimura, K. Tago, T. Hoshi, and Y. Matsushita. A Method of Appliance Detection Based on Features of Power Waveform. In 4th Intl. Conference on Symposium on Applications and the Internet (SAINT), pages 291--294. IEEE, 2004.Google ScholarCross Ref
- M. Jahn, M. Jentsch, C. Prause, F. Pramudianto, A. Al-Akkad, and R. Reiners. The Energy Aware Smart Home. In 5th Intl. Conference on Future Information Technology (FutureTech), pages 1--8. IEEE, 2010.Google Scholar
- L. Jiang, S. Luo, and J. Li. An Approach of Household Power Appliance Monitoring Based on Machine Learning. In 5th Intl. Conference on Intelligent Computation Technology and Automation (ICICTA), pages 577--580. {IEEE}, 2012. Google ScholarDigital Library
- X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler. Design and Implementation of a High-Fidelity AC Metering Network. In 8th Intl. Conference on Information Processing in Sensor Networks (IPSN), pages 253--264. {IEEE}, 2009. Google ScholarDigital Library
- Y. Kim, T. Schmid, Z. M. Charbiwala, and M. B. Srivastava. ViridiScope: Design and Implementation of a Fine Grained Power Monitoring System for Homes. In 11th Intl. Conference on Ubiquitous Computing (UbiComp), pages 245--254. {ACM}, 2009. Google ScholarDigital Library
- D. Kirschen. Demand-Side View of Electricity Markets. IEEE Transactions on Power Systems, 18(2):520--527, 2003.Google ScholarCross Ref
- W. Kleiminger, S. Santini, and M. Weiss. Opportunistic Sensing for Smart Heating Control in Private Households. In 2nd Intl. Workshop on Networks of Cooperating Objects (CONET), 2011.Google Scholar
- Kolter, Zico and Johnson, Matthew. REDD: A Public Data Set for Energy Disaggregation Research. In 1st Intl. Workshop on Data Mining Applicatins in Sustainability (SustKDD). ACM, 2011.Google Scholar
- C. Laughman, K. Lee, R. Cox, S. Shaw, S. Leeb, L. Norford, and P. Armstrong. Power Signature Analysis. IEEE Power and Energy Magazine, 1(2):56--63, 2003.Google ScholarCross Ref
- K. Lee, S. Leeb, L. Norford, P. Armstrong, J. Holloway, and S. Shaw. Estimation of Variable-Speed-Drive Power Consumption from Harmonic Content. IEEE Transactions on Energy Conversion, 20(3):566--574, 2005.Google ScholarCross Ref
- T. Mantoro, M. A. Ayu, and E. E. Elnour. Web-Enabled Smart Home Using Wireless Node Infrastructure. In 9th Intl. Conference on Advances in Mobile Computing and Multimedia (MoMM), pages 72--79. ACM, 2011. Google ScholarDigital Library
- J. Nichols and B. Myers. Controlling Home and Office Appliances with Smart Phones. Pervasive Computing, 5(3):60--67, 2006. Google ScholarDigital Library
- S. Patel, T. Robertson, J. Kientz, M. Reynolds, and G. Abowd. At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line. In 9th Intl. Conference on Ubiquitous Computing (UbiComp), pages 271--288. {ACM}, 2007. Google ScholarDigital Library
- J. Pierce, D. Schiano, and E. Paulos. Home, Habits, and Energy: Examining Domestic Interactions and Energy Consumption. In 28th Intl. Conference on Human Factors in Computing Systems (CHI), pages 1985--1994. ACM, 2010. Google ScholarDigital Library
- A. Reinhardt, P. Baumann, D. Burgstahler, M. Hollick, H. Chonov, M. Werner, and R. Steinmetz. On the Accuracy of Appliance Identification Based on Distributed Load Metering Data. Lamp, 6:45, 2012.Google Scholar
- I. Witten, E. Frank, and M. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2011. Google ScholarDigital Library
- M. Zeifman and K. Roth. Nonintrusive Appliance Load Monitoring: Review and Outlook. IEEE Transactions on Consumer Electronics, 57(1):76--84, 2011. Google ScholarDigital Library
- S. Ziegler, R. Woodward, H. Iu, and L. Borle. Current Sensing Techniques: A Review. IEEE Sensors Journal, 9(4):354--376, 2009.Google ScholarCross Ref
Index Terms
- How to auto-configure your smart home?: high-resolution power measurements to the rescue
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