@Article{2019.100000113,
AUTHOR = {Weilie Liu, Jialing He, Meng Li, Rui Jin, Jingjing Hu, Zijian Zhang},
TITLE = {An Efficient Supervised Energy Disaggregation Scheme for Power Service  in Smart Grid},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {3},
PAGES = {585--593},
URL = {http://www.techscience.com/iasc/v25n3/39687},
ISSN = {2326-005X},
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 to learn appliance feature 
matrices. Additionally, we improved the max-min pruning matching optimization 
algorithm to decompose the aggregate power consumption into individual 
appliance. Experimental results obtained using the reference energy 
disaggregation dataset demonstrate that the proposed scheme achieves 81.9% 
accuracy and requires only 8 s to analyze 20-m readings for each sliding 
window. Thus, the proposed scheme exhibits better accuracy and efficiency 
compared with existing schemes.},
DOI = {10.31209/2019.100000113}
}