A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images

Abstract

Non-intrusive load monitoring (NILM) has been on the rise for more than three decades. Its main objective is non-intrusive load disaggregation into individual operating appliances. Recent studies have shown that a higher sampling rate in the aggregated measurements allows better performance regarding load disaggregation. In addition, recent developments in deep learning and, in particular, convolutional neural networks (CNNs) have facilitated load disaggregation using CNN models. Several methods have been described in the literature that combine both a higher sampling rate and a CNN-based NILM framework. However, these methods use only a small number of cycles of the aggregated signal, which complicates the practical application of real-time NILM. In this work, a high sampling rate time-driven CNN-based NILM framework is also proposed. However, a novel current harmonic distortion image extracted from 60 cycles of the aggregated signal is proposed, resulting in 1 s appliance classification with low computational complexity. Appliance classification performance is evaluated using the PLAID3 dataset for both single and combined appliance operation. In addition, a comparison is made with a method from the literature. The results highlight the robustness of the novel feature and confirm the real-time applicability of the proposed NILM framework.