Evolving non-intrusive load monitoring

Authors:
Dominik Egarter;Anita Sobe;Wilfried Elmenreich
Affiliations:
Institute of Networked and Embedded Systems / Lakeside Labs, Alpen-Adria-Universität Klagenfurt, Austria;Institut d'informatique, Université de Neuchâtel, Switzerland;Institute of Networked and Embedded Systems / Lakeside Labs, Alpen-Adria-Universität Klagenfurt, Austria, Complex Systems Engineering, Universität Passau, Germany
Venue:
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Year:
2013

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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.