Non-Intrusive Electric Load Monitoring Approach Based on Current Feature Visualization for Smart Energy Management
This addresses energy management in smart cities by enabling non-invasive monitoring of electric loads for users, though it appears incremental as it combines existing techniques like computer vision and signal processing.
The paper tackles non-intrusive electric load monitoring for smart energy management by mapping current signals to color images using signal transforms and Gramian Angular Field, then recognizing loads with a U-shape deep neural network. Experimental results show it achieves superior performance on public and private datasets, supporting efficient energy management in IoT.
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).