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. © Springer-Verlag Berlin Heidelberg 2013.
Author supplied keywords
Cite
CITATION STYLE
Egarter, D., Sobe, A., & Elmenreich, W. (2013). Evolving non-intrusive load monitoring. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7835 LNCS, pp. 182–191). Springer Verlag. https://doi.org/10.1007/978-3-642-37192-9_19
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
