Non-Intrusive Electric Load Monitoring Approach Based on Current Feature
  Visualization for Smart Energy Management
Yiwen Xu Dengfeng Liu Liangtao Huang Zhiquan Lin Tiesong Zhao Sam Kwong
The state-of-the-art smart city has been calling for an economic but
efficient energy management over large-scale network, especially for the
electric power system. It is a critical issue to monitor, analyze and control
electric loads of all users in system. In this paper, we employ the popular
computer vision techniques of AI to design a non-invasive load monitoring
method for smart electric energy management. First of all, we utilize both
signal transforms (including wavelet transform and discrete Fourier transform)
and Gramian Angular Field (GAF) methods to map one-dimensional current signals
onto two-dimensional color feature images. Second, we propose to recognize all
electric loads from color feature images using a U-shape deep neural network
with multi-scale feature extraction and attention mechanism. Third, we design
our method as a cloud-based, non-invasive monitoring of all users, thereby
saving energy cost during electric power system control. Experimental results
on both public and our private datasets have demonstrated our method achieves
superior performances than its peers, and thus supports efficient energy
management over large-scale Internet of Things (IoT).