Open Access
ARTICLE
An Efficient Supervised Energy Disaggregation Scheme for Power Service in Smart Grid
Weilie Liu, Jialing He, Meng Li, Rui Jin, Jingjing Hu, Zijian Zhang
School of Computer Science and Technology Beijing Institute of Technology
No.5 Yard Zhong Guan Cun South Street Haidian District Beijing China
* Corresponding Author: Jingjing Hu,
Intelligent Automation & Soft Computing 2019, 25(3), 585-593. https://doi.org/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 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.
Keywords
Cite This Article
W. Liu, J. He, M. Li, R. Jin, J. Hu
et al., "An efficient supervised energy disaggregation scheme for power service in smart grid,"
Intelligent Automation & Soft Computing, vol. 25, no.3, pp. 585–593, 2019.