Abstract
Non-intrusive load monitoring (NILM) identifies used appliances in a total power load according to their individual load characteristics. In this paper we propose an evolutionary optimization algorithm to identify appliances, which are modeled as on/off appliances. We evaluate our proposed evolutionary optimization by simulation with Matlab, where we use a random total load and randomly generated power profiles to make a statement of the applicability of the evolutionary algorithm as optimization technique for NILM. Our results shows that the evolutionary approach is feasible to be used in NILM systems and can reach satisfying detection probabilities.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baranski, M., Voss, J.: Genetic algorithm for pattern detection in nialm systems. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3462–3468 (2004)
Bijker, A., Xia, X., Zhang, J.: Active power residential non-intrusive appliance load monitoring system. In: AFRICON 2009, pp. 1–6 (September 2009)
Chang, H.H., Chien, P.C., Lin, L.S., Chen, N.: Feature extraction of non-intrusive load-monitoring system using genetic algorithm in smart meters. In: IEEE 8th International Conference on e-Business Engineering (ICEBE), pp. 299–304 (2011)
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: 14th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 27–32 (2010)
Elmenreich, W., Egarter, D.: Design guidelines for smart appliances. In: Proc. 10th International Workshop on Intelligent Solutions in Embedded Systems (WISES 2012), pp. 76–82 (2012)
Garey, M.R., Johnson, D.S.: Computers and Intractability; A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1990)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Hart, G.: Nonintrusive appliance load monitoring. Proceedings of the IEEE 80(12), 1870–1891 (1992)
Hoff, A., Løkketangen, A., Mittet, I.: Genetic Algorithms for 0/1 Multidimensional Knapsack Problems. In: Proceedings Norsk Informatikk Konferanse, NIK 1966 (1996)
Kolter, J.Z., Johnson, M.J.: REDD: A Public Data Set for Energy Disaggregation Research. In: Proceedings of the SustKDD Workshop on Data Mining Applications in Sustainability (2011)
Lagoudakis, M.G.: The 0-1 Knapsack Problem An Introductory Survey. Technical report
Leung, S.K.J., Ng, S.H.K., Cheng, W.M.J.: Identifying Appliances Using Load Signatures and Genetic Algorithms. In: International Conference on Electrical Engineering, ICEE (2007)
Lin, Y.H., Tsai, M.S., Chen, C.S.: Applications of fuzzy classification with fuzzy c-means clustering and optimization strategies for load identification in nilm systems. In: 2011 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 859–866 (2011)
Martello, S., Toth, P.: Knapsack problems: algorithms and computer implementations. John Wiley & Sons, Inc., New York (1990)
Singh, R.P.: Solving 0/1 Knapsack problem using Genetic Algorithms. In: IEEE 3rd International Conference on Communication Software and Networks (ICCSN), pp. 591–595. IEEE (2011)
Suzuki, K., Inagaki, S., Suzuki, T., Nakamura, H., Ito, K.: Nonintrusive appliance load monitoring based on integer programming. In: SICE Annual Conference, pp. 2742–2747 (2008)
Cotta, C., Troya, J.: A hybrid genetic algorithm for the 0-1 multiple knapsack problem. Artificial Neural Nets and Genetic Algorithms 3, 251–255 (1998)
Zeifman, M., Roth, K.: Nonintrusive appliance load monitoring: Review and outlook. IEEE Transactions on Consumer Electronics 57(1), 76–84 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Egarter, D., Sobe, A., Elmenreich, W. (2013). Evolving Non-Intrusive Load Monitoring. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-37192-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37191-2
Online ISBN: 978-3-642-37192-9
eBook Packages: Computer ScienceComputer Science (R0)