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The state-of-the-art smart city has been calling for an economic butefficient energy management over large-scale network, especially for theelectric power system. It is a critical issue to monitor, analyze and controlelectric loads of all users in system. In this paper, we employ the popularcomputer vision techniques of AI to design a non-invasive load monitoringmethod for smart electric energy management. First of all, we utilize bothsignal transforms (including wavelet transform and discrete Fourier transform)and Gramian Angular Field (GAF) methods to map one-dimensional current signalsonto two-dimensional color feature images. Second, we propose to recognize allelectric loads from color feature images using a U-shape deep neural networkwith multi-scale feature extraction and attention mechanism. Third, we designour method as a cloud-based, non-invasive monitoring of all users, therebysaving energy cost during electric power system control. Experimental resultson both public and our private datasets have demonstrated our method achievessuperior performances than its peers, and thus supports efficient energymanagement over large-scale Internet of Things (IoT).

Non-Intrusive Electric Load Monitoring Approach Based on Current Feature Visualization for Smart Energy Management