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

TL;DR

This paper introduces a non-intrusive, AI-based electric load monitoring method that transforms current signals into visual features for efficient, large-scale energy management in smart grids.

Contribution

It presents a novel approach combining signal transformation and deep learning for non-invasive electric load recognition in smart energy systems.

Findings

Achieves superior load recognition accuracy compared to existing methods.

Demonstrates effective energy management in IoT-based smart grids.

Validates approach on public and private datasets.

Abstract

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).