A digital twin-based framework for load identification using odd harmonic current plots

Abstract Non-intrusive Load Monitoring (NILM) techniques are becoming more and more widespread, because of the interest that consumers have in efficient energy consumption and management. At the same time, NILM application along with Demand Side Management (DSM) schemes could face Distribution Network (DN) operational issues like congestion management. The advent of Digital Twin (DT) technology offers a sustainable solution for more effective energy management in real-time applications. In addition, recent developments in NILM suggest that high sampling rates of the aggregated extracted signal could enable better performance for load disaggregation. This work explores DT integration with NILM for a real-time appliance classification scheme. More specifically, a Convolutional Neural Network (CNN) model fed with images that depict odd current harmonics is utilized to classify the appliance(s) operation. The images are extracted exploiting the high sampling measurements provided by the PLAID dataset. Three different scenarios that include various residential appliances are examined comprising both single and combined appliance operation, as well as event detection (appliance’s activation/de-activation). The results of the proposed high sampling DT-based NILM framework show: (a) a remarkably good performance of the model, despite the limited data, proving that the utilization of harmonics contributes to an improved classification, and (b) the applicability of the model to real-time applications given that the whole procedure from initial data processing to image classification (i.e., appliance identification) lasts less than 1 s.