An efficient, scalable time-frequency method for tracking energy usage of domestic appliances using a two-step classification algorithm

Energies

Volumn 7, Issue 11, 2014, Pages 7041-7066

  Meehan, Paula ^a   McArdle, Conor ^a   Daniels, Stephen ^a  

^a DUBLIN CITY UNIVERSITY   (Ireland)

Author keywords

Appliance identification; Current harmonics; Load monitoring; Transient signal

Indexed keywords

AGGREGATES; ELECTRIC LOAD MANAGEMENT; ELECTRIC VARIABLES MEASUREMENT; ENERGY UTILIZATION; POWER QUALITY;

CLASSIFICATION ALGORITHM; CURRENT HARMONICS; ELECTRICAL CHARACTERISTIC; IDENTIFICATION ALGORITHMS; LOAD MONITORING; LOAD MONITORING SYSTEMS; TIME-FREQUENCY METHODS; TRANSIENT SIGNAL;

DOMESTIC APPLIANCES;

EID: 84912006362     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en7117041     Document Type: Article

References (36)

1. European Environmental Agency. Final Energy Consumption by Sector in the EU-27, 1990-2006. Available online: http://www.eea.europa.eu/data-and-maps/figures/final-energy-consumption-by-sector-in-the-eu-27-1990-2006 (accessed on 12 December 2012).
2. U.S Energy Information Administration. Today in Energy: Heating and Cooling No Longer Majority of U.S. Home Energy Use. Available online: http://www.eia.gov/todayinenergy/ detail.cfm?id=10271 (accessed on 28 October 2014).
3. Darby, S. The Effectiveness of Feedback on Energy Consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays; University of Oxford: Oxford, UK, 2006.
4. Mynatt, E.D.; Rowan, J.; Craighill, S.; Jacobs, A. Digital family portraits: Supporting peace of mind for extended family members. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, Seattle, WA, USA, 31 March-5 April 2001; pp. 333-340.
5. Tapia, E.M.; Intille, S.S; Larson, K. Activity recognition in the home using simple and ubiquitous sensors. In Pervasive Computing; Springer: Berlin, Germany, 2004.
6. Carlson, D.R.; Bergés, M.; Matthews, H.S. How many appliances does it take to...? In Proceedings of the 1st International Workshop on Non-Intrusive Load Monitoring, Pittsburgh, PA, USA, 7 May 2012.
7. ESB Networks Ireland. Frequently Asked Questions: What is the Electricity Voltage in Ireland? Available online: http://www.esb.ie/esbnetworks/en/about-us/faqs.jsp (accessed on 28 October 2014).
8. Chen, K.L.; Chang, H.H.; Chen, N. A new transient feature extraction method of power signatures for Nonintrusive Load Monitoring Systems. In Proceedings of the 2013 IEEE InternationalWorkshop on Applied Measurements for Power Systems (AMPS), Aachen, Germany, 25-27 September 2013; pp. 79-84.
9. Chang, H.H. Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies 2012, 5, 4569-4589.
10. Chang, H.H.; Chen, K.L.; Tsai, Y.P.; Lee, W.J. A new measurement method for power signatures of nonintrusive demand monitoring and load identification. IEEE Trans. Ind. Appl. 2012, 48, 764-771.
11. Chang, H.H.; Lin, C.L.; Lee, J.K. Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms. In Proceedings of the 2010 14th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Shanghai, China, 14-16 April 2010; pp. 27-32.
12. Chang, H.H.; Yang, H.T.; Lin, C.L. Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. In Computer Supported Cooperative Work in Design IV; Springer: Berlin, Germany, 2008; pp. 664-674.
13. Gupta, S.; Reynolds, M.; Patel, S. ElectriSense: Single-point sensing using EMI for electrical event detection and classification in the home. In Proceedings of the International Conference on Ubiquitous Computing, ACM, Copenhagen, Denmark, 26-29 September 2010; pp. 139-148.
14. Patel, S.; Robertson, T.; Kientz, J.; Reynolds, M.; Abowd, G. At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In Proceedings of the International Conference on Ubiquitous Computing, Innsbruck, Austria, 16-19 September 2007; pp. 271-288.
15. Srinivasan, D.; Ng, W.; Liew, A. Neural-network-based signature recognition for harmonic source identification. IEEE Trans. Power Deliv. 2006, 21, 398-405.
16. Parson, O.; Ghosh, S.; Weal, M.; Rogers, A. Using hidden Markov models for iterative non-intrusive appliance monitoring. In Proceedings of Neural Information Processing Systems workshop on Machine Learning for Sustainability, Sierra Nevada, Spain, 17 December 2011.
17. Parson, O.; Ghosh, S.; Weal, M.; Rogers, A. Non-intrusive load monitoring using prior models of general appliance types. In Proceedings of theTwenty-Sixth Conference on Artificial Intelligence (AAAI-12), Toronto, CA, USA, 22-26 July 2012; pp. 356-362.
18. Zoha, A.; Gluhak, A.; Nati, M.; Imran, M.A. Low-power appliance monitoring using factorial Hidden Markov models. In Proceedings of 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia, 2-5 April 2013; pp. 527-532.
19. Liang, J.; Ng, S.; Kendall, G.; Cheng, J. Load signature study-Part I: Basic concept, structure, and methodology. IEEE Trans. Power Deliv. 2010, 25, 551-560.
20. Liang, J.; Ng, S.; Kendall, G.; Cheng, J. Load signature study-Part II: Disaggregation framework, simulation, and applications. IEEE Trans. Power Deliv. 2010, 25, 561-569.
21. Zeifman, M.; Roth, K. Nonintrusive appliance load monitoring: Review and outlook. IEEE Trans. Consum. Electron. 2011, 57, 76-84.
22. Zoha, A.; Gluhak, A.; Imran, M.A.; Rajasegarar, S. Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors 2012, 12, 16838-16866.
23. Hart, G.W. Nonintrusive appliance load monitoring. Proc. IEEE 1992, 80, 1870-1891.
24. Weiss, M.; Helfenstein, A.; Mattern, F.; Staake, T. Leveraging smart meter data to recognize home appliances. In Proceedings of the 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom), Ticino, Switzerland, 19-23 March 2012; pp. 190-197.
25. Department of Energy and Climate Change, United Kingdom. Household Electricity Survey. Available online: https://www.gov.uk/government/publications/household-electricity-survey (accessed on 28 October 2014).
26. Powers, J.; Margossian, B.; Smith, B. Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data. IEEE Comput. Appl. Power 1991, 4, 42-47.
27. Key, T.S.; Lai, J.S. Comparison of standards and power supply design options for limiting harmonic distortion in power systems. IEEE Trans. Ind. Appl. 1993, 29, 688-695.
28. Zhang, H. The Optimality of Naive Bayes; American Association for Artificial Intelligence (AAAI): Palo Alto, CA, USA, 2004.
29. Wu, X.; Kumar, V.; Quinlan, J.R.; Ghosh, J.; Yang, Q.; Motoda, H.; McLachlan, G.J.; Ng, A.; Liu, B.; Philip, S.Y.; et al. Top 10 algorithms in data mining. Knowl. Inf. Syst. 2008, 14, 1-37.
30. Domingos, P.; Pazzani, M. Beyond independence: Conditions for the optimality of the simple bayesian classifier. In Proceedings of the 13th International Conference Machine Learning, Bari, Italy, 3-6 July 1996; pp. 105-112.
31. Marchiori, A.; Hakkarinen, D.; Han, Q.; Earle, L. Circuit-level load monitoring for household energy management. IEEE Pervasive Comput. 2011, 10, 40-48.
32. Reinhardt, A.; Burkhardt, D.; Zaheer, M.; Steinmetz, R. Electric appliance classification based on distributed high resolution current sensing. In Proceedings of the 2012 IEEE 37th Conference on Local Computer Networks Workshops (LCN Workshops), Clearwater, FL, USA, 22-25 October 2012; pp. 999-1005.
33. Allegro MicroSystems, LLC. ACS712: Fully Integrated, Hall-Effect-Based Linear Current Sensor IC with 2.1 kVRMS Voltage Isolation and a Low-Resistance Current Conductor. Available online: http://www.allegromicro.com/Products/Current-Sensor-ICs/Zero-To-Fifty-Amp-Integrated-Conductor-Sensor-ICs/ACS712.aspx (accessed on 15 October 2013).
34. LabJack Corporation. UE9/UE9-Pro: Multifunction DAQ with Ethernet and USB. Available online: http://labjack.com/ue9 (accessed on 16 October 2013).
35. Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006, 27, 861-874.
36. Anderson, K.; Ocneanu, A.; Benitez, D.; Carlson, D.; Rowe, A.; Berges, M. BLUED: A fully labeled public dataset for event-based non-intrusive load monitoring research. In Proceedings of the 2nd KDDWorkshop on Data Mining Applications in Sustainability (SustKDD), Beijing, China, 12 August 2012.