Abstract
The aim of reducing greenhouse gases and increasing energy efficiency faces a number of challenges to date. A significant portion of overall energy expenditure in residential and commercial sectors is considered as wastage. Finding technological methods in order to reduce wastage has been the main focus of researchers in recent years. Non-Intrusive Load Monitoring (NILM) is perceived as a cost-effective approach to monitor appliance level energy consumption in a building. However, this approach still faces a number of problems that need to be addressed. In this study, we propose an approach by which uncertainty of appliance’s identification that have similar signatures, is addressed. Unlike other approaches, our approach uses occupant’s behavioural information to aid appliance disaggregation algorithms. We also demonstrate our technique through experimentation in a household.
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References
Current Cost EnviR, http://www.currentcost.com/product-envir.html (last accessed October 10, 2013)
Darby, S.: The effectiveness of feedback on energy consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays 486 (2006)
U.S. Energy Information Administration, http://www.eia.gov (last accessed October 10, 2013)
European Commission Statistical book on Environment and Energy, http://ec.europa.eu (last accessed September 14, 2011)
Froehlich, J., Larson, E., Gupta, S., Cohn, G., Reynolds, M., Patel, S.: Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Computing 10(1), 28–39 (2011)
Gupta, S., Reynolds, M.S., Patel, S.N.: Electrisense: single-point sensing using emi for electrical event detection and classification in the home. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 139–148. ACM, New York (2010)
Hart, G.W.: Nonintrusive appliance load monitoring. Proceedings of the IEEE 80(12) (1992)
Marchiori, A., Hakkarinen, D., Han, Q., Earle, L.: Circuit-level load monitoring for household energy management. IEEE Pervasive Computing 10(1), 40–48 (2011)
O’Hare, G.M.P., O’Grady, M.J., Keegan, S., O’Kane, D., Tynan, R., Marsh, D.: Intelligent agile agents: Active enablers for ambient intelligence. In: ACM’s Special Interest Group on Computer-Human Interaction (SIGCHI), Ambient Intelligence for Scientific Discovery (AISD) Workshop, Vienna (2004)
Patel, S.N., Robertson, T., Kientz, J.A., Reynolds, M.S., Abowd, G.D.: At the flick of a switch: Detecting and classifying unique electrical events on the residential power line. In: Krumm, J., Abowd, G.D., Seneviratne, A., Strang, T. (eds.) UbiComp 2007. LNCS, vol. 4717, pp. 271–288. Springer, Heidelberg (2007)
Ruzzelli, A., Nicolas, C., Schoofs, A., O’Hare, G.: Real-time recognition and profiling of appliances through a single electricity sensor. In: 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks, pp. 1–9 (2010)
Williams, E., Matthews, H.: Scoping the potential of monitoring and control technologies to reduce energy use in homes. In: Proceedings of the IEEE International Symposium on Electronics the Environment, pp. 239–244 (2007)
Zeifman, M., Roth, K.: Nonintrusive appliance load monitoring: Review and outlook. IEEE Transactions on Consumer Electronics 57(1), 76–84 (2011)
Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors 12(12), 16838–16866 (2012), http://www.mdpi.com/1424-8220/12/12/16838
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Kazmi, A.H., O’Grady, M.J., O’Hare, G.M.P. (2013). Non-intrusive Identification of Electrical Appliances. In: O’Grady, M.J., et al. Evolving Ambient Intelligence. AmI 2013. Communications in Computer and Information Science, vol 413. Springer, Cham. https://doi.org/10.1007/978-3-319-04406-4_18
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DOI: https://doi.org/10.1007/978-3-319-04406-4_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04405-7
Online ISBN: 978-3-319-04406-4
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