Weilie Liu, Jialing He, Meng Li, Rui Jin, Jingjing Hu, Zijian Zhang
Intelligent Automation & Soft Computing, Vol.25, No.3, pp. 585-593, 2019, DOI:10.31209/2019.100000113
Abstract Smart energy disaggregation is receiving increasing attention because it can be
used to save energy and mine consumer's electricity privacy by decomposing
aggregated meter readings. Many smart energy disaggregation schemes have
been proposed; however, the accuracy and efficiency of these methods need to
be improved. In this work, we consider a supervised energy disaggregation
method which initially learns the power consumption of each appliance and
then disaggregates meter readings using the previous learning result. In this
study, we improved the fast search and find of density peaks clustering
algorithm to cluster appliance power signals twice More >
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