A blind event-based learning algorithm for non-intrusive load disaggregation

نویسندگان

چکیده

Non-intrusive loading monitoring (NILM) provides a smart solution to the problem of electrical energy households at appliance level. In blind disaggregation, power level each is not known priori. this paper, we propose an event-based disaggregation algorithm that uses Gaussian mixture models (GMM) for clustering automatically detect two-state appliances from aggregate data. The benefit using over other methods they can learn statistical distributions present in This beneficial, especially when have similar consumptions. Since do determine number clusters automatically, use Bayesian information criteria (BIC) clusters. method tested with data real house collected by meter which samples consumption 3.4 kHz and also Reference Energy Disaggregation Dataset (REDD) public data, sampled frequency 1 Hz. We compared performance our unsupervised found comparable performance. model mean shift disaggregation. saw improved instead mean-shift various accuracy measures.

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ژورنال

عنوان ژورنال: International Journal of Electrical Power & Energy Systems

سال: 2021

ISSN: ['1879-3517', '0142-0615']

DOI: https://doi.org/10.1016/j.ijepes.2021.106834