User-centered nonintrusive electricity load monitoring for residential buildings

Journal of Computing in Civil Engineering

Volumn 25, Issue 6, 2011, Pages 471-480

  Berges, Mario ^a   Goldman, Ethan ^b   Matthews, H Scott ^a   Soibelman, Lucio ^a   Anderson, Kyle ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b Vermont Energy Investment Corporation   (United States)

Author keywords

Demand side management; Electrical load signatures; Energy management; Nonintrusive load monitoring; Power systems; Signal processing

Indexed keywords

CLASSIFICATION TASKS; CURRENT MEASUREMENTS; DATA SETS; DEMAND SIDE MANAGEMENTS; ELECTRICAL APPLIANCES; ELECTRICAL LOAD; ELECTRICITY LOAD; END USERS; FALSE POSITIVE RATES; MAIN CIRCUIT; NON-INTRUSIVE; NONINTRUSIVE LOAD MONITORING; POSSIBLE FUTURES; POWER STRIPS; PROTOTYPE SYSTEM; RESIDENTIAL BUILDING; SIMPLE ALGORITHM; TRUE POSITIVE; USER INPUT; USER INTERACTION; USER-CENTERED;

ALGORITHMS; BUILDINGS; ELECTRIC LOAD MANAGEMENT; ELECTRIC UTILITIES; ENERGY MANAGEMENT; ENERGY UTILIZATION; SIGNAL PROCESSING; STANDBY POWER SYSTEMS;

MONITORING;

EID: 84055212433     PISSN: 08873801     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)CP.1943-5487.0000108     Document Type: Conference Paper

References (38)

1. Baranski, M., and Voss, J. (2004). Genetic algorithm for pattern detection in NIALM systems.. Proc., 2004 IEEE Int. Conf. on Systems, Man and Cybernetics, IEEE, New York, Vol. 4, 3462-3468 http://ieeexplore.ieee.org/iel5/ 9622/30424/01400878.pdf (Oct. 1, 2007). (Pubitemid 40408370)
2. Berges, M., Goldman, E., Matthews, H. S., and Soibelman, L. (2010 a). Enhancing electricity audits in residential buildings with non-intrusive load monitoring.. J. Ind. Ecol., 1088-1980 14 (5), 844-858.
3. Berges, M., Soibelman, L., and Matthews, H. S. (2010 b). Leveraging data from environmental sensors to enhance electrical load disaggregation algorithms.. Proc., 13th Int. Conf., Computing in Civil and Building Engineering, Nottingham University, UK.
4. Berges, M., Soibelman, L., Matthews, H. S., and Goldman, E. (2010 c). Evaluating the electric consumption of residential buildings: Current practices and future prospects.. Proc., Construction Research Congress 2010, ASCE, Reston, VA.
5. Bishop, C. M. (2007). Pattern recognition and machine learning, 1st Ed., Springer, New York.
6. Chang, H., Lin, C., and Yang, H. (2008). Load recognition for different loads with the same real power and reactive power in a non-intrusive load-monitoring system.. Proc., 12th Int. Conf., Computer Supported Cooperative Work in Design 2008. IEEE, New York, 1122-1127. http://ieeexplore.ieee.org/iel5/ 4530852/4536938/04537137.pdf?arnumber=4537137 (Oct. 1, 2007).
7. Committee on America's Energy Future; National Academy of Sciences; National Academy of Engineering; National Research Council. (2009). America's energy future: Technology and transformation, Summary Ed., National Academies Press, Washington, DC.
8. Cox, R., Leeb, S. B., Shaw, S. R., and Norford, L. K. (2006). Transient event detection for nonintrusive load monitoring and demand side management using voltage distortion.. Proc., 21st Annual IEEE Conf.: Applied Power Electronics, New York.
9. Darby, S. (2006). The effectiveness of feedback on energy consumption, Environmental Change Institute, Univ. of Oxford, Oxford, UK. http://www.eci.ox.ac.uk/research/energy/electric-metering.php (Aug. 17, 2009).
10. Electric Power Research Institute (EPRI). (1997 a). Low-cost NIALMS technology: Market issues and product assessment.. TR-108918-V1, Palo Alto, CA. http://mydocs.epri.com/docs/public/TR-108918-V1.pdf (Apr. 30, 2009).
11. Electric Power Research Institute (EPRI). (1997 b). Low-cost NIALMS technology: Technical assessment.. TR-108918-V2, Palo Alto, CA. http://mydocs.epri.com/docs/public/TR-108918-V2.pdf (Apr. 30, 2009).
12. Energy Information Administration. (2001). End-use consumption of electricity by end use and appliance.. Washington, DC. http://www.eia.doe.gov/ emeu/recs/recs2001/enduse2001/enduse2001.html (Feb. 19, 2009).
13. Farinaccio, L., and Zmeureanu, R. (1999). Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses.. Energy Build., 30 (3), 245-259. 0378-7788 (Pubitemid 30509852)
14. Fischer, C. (2008). Feedback on household electricity consumption: A tool for saving energy? Energ. Effic. 1570-646X, 1 (1), 79-104. 10.1007/s12053-008- 9009-7 (Pubitemid 351796268)
15. Greene, K. (2010). Technology review: Home sensor startup snapped up.. Massachusetts Institute of Technology (MIT), Cambridge, MA. http://www. technologyreview.com/energy/25205/ (Oct. 6, 2010).
16. Hart, G. (1989). Residential energy monitoring and computerized surveillance via utility power flows.. IEEE Technol. Soc. Mag. 0278-0097, 8 (2), 12-16. 10.1109/44.31557 (Pubitemid 20605449)
17. Hart, G. (1992). Nonintrusive appliance load monitoring.. Proc. IEEE, 80 (12), 1870-1891. 0018-9219 (Pubitemid 23612231)
18. Intel. (2010). Intel's experimental sensor analyzes appliance power consumption from single outlet.. http://www.engadget.com/2010/04/15/intels- experimental-sensor-analyzes-appliance-power-consumption/ (Oct. 6, 2010).
19. Labview 2010 (Build number: 10.0.0.4032) [Computer software]. Austin, TX, National Instruments.
20. Lam, H., Fung, G., and Lee, W. (2007). A novel method to construct taxonomy electrical appliances based on load signatures.. IEEE Trans. Consum. Electron. 0098-3063, 53 (2), 653-660. (Pubitemid 47215083)
21. Laughman, C., (2003). Power signature analysis.. IEEE Power Energ. Mag. 1540-7977, 1 (2), 56-63. 10.1109/MPAE.2003.1192027
22. Lawrence Livermore National Laboratory. (2009). Energy flow charts.. https://publicaffairs.llnl.gov/news/energy/energy.html (Mar. 29, 2010).
23. Lee, K. D. (2003). Electric load information system based on non-intrusive power monitoring.. Ph.D. thesis, Dept. of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA. http://dspace.mit.edu/ handle/1721.1/29633 (Mar. 26, 2009).
24. Leeb, S., Shaw, S., and Kirtley, J. (1995). Transient event detection in spectral envelope estimates for nonintrusive load monitoring.. IEEE Trans. Power Delivery 0885-8977, 10 (3), 1200-1210.
25. Luo, D., (2002). Monitoring HVAC equipment electrical loads from a centralized location-Methods and field test results.. ASHRAE Transactions, 108 (1), 841-857. 0001-2505 (Pubitemid 34613739)
26. MATLAB 7.9. Natick, MA, MathWorks.
27. Matthews, H. S., Soibelman, L., Berges, M., and Goldman, E. (2008). Automatically disaggregating the total electrical load in residential buildings: A profile of the required solution.. Proc., Intelligent Computing in Engineering (ICE08), European Group for Intelligent Computing in Engineering, Lausanne, Switzerland, 381-389. http://www.eg-ice.org/Workshops/ICE08/ papers%20pdf/P014V03.pdf.
28. Mitchell, T. (1997). Machine learning, 1st Ed., McGraw Hill Higher Education, Burr Ridge, IL.
29. Mitchell, T. (2005). Generative and discriminative classifiers: Nave Bayes and logistic regression.. Machine learning. http://www.cs.cmu.edu/%7Etom/ mlbook/NBayesLogReg.pdf.
30. Norford, L. K., and Leeb, S. B. (1996). Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms.. Energy Build., 24 (1), 51-64. 0378-7788 (Pubitemid 126339890)
31. Paris, J. (2006). A framework for non-intrusive load monitoring and diagnostics.. M.S. thesis, Dept. of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA. http://dspace.mit.edu/ handle/1721.1/37081 (Nov. 14, 2008).
32. Parker, D., Hoak, D., Meier, A., and Brown, R. (2006). How much energy are we using? Potential of residential energy demand feedback devices.. Proc., 2006 Summer Study on Energy Efficiency in Buildings, American Council for an Energy Efficiency Economy, Washington, DC.
33. Peschiera, G., Taylor, J. E., and Siegel, J. A. (2010). Response-relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data.. Energy Build., 42 (8), 1329-1336. 0378-7788
34. Prudenzi, A. (2002). A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel.. Power Engineering Society Winter Meeting, 2002. IEEE, New York, Vol. 2, 941-946.
35. Shaw, S. R., Leeb, S. B., Norford, L. K., and Cox, R. W. (2008). Nonintrusive load monitoring and diagnostics in power systems.. IEEE Trans. Instrum. Meas. 0018-9456, 57 (7), 1445-1454.
36. Smith, V. A., Norford, L., and Leeb, S. (2003). Final report compilation for equipment scheduling and cycling.. California Energy Commission. http://www.archenergy.com/cec-eeb/P2-Diagnostics/P2-Diagnostics-Reports/P2/D-2. 2-Compiled.pdf (Apr. 30, 2009).
37. Srinivasan, D., Ng, W., and Liew, A. (2006). Neural-network-based signature recognition for harmonic source identification.. IEEE Trans. Power Delivery 0885-8977, 21 (1), 398-405. (Pubitemid 43279826)
38. Wichakool, W., (2007). Resolving power consumption of variable power electronic loads using nonintrusive monitoring.. Proc., Power Electronics Specialists Conf., IEEE, New York, 2765-2771. http://ieeexplore.ieee.org/stamp/ stamp.jsp?arnumber=4342456&isnumber=4341940 (Jul. 11, 2008).