Features Mining and Machine Learning for Home Appliance Identification by Processing Smart meter Data

Abstract

The energy sector is changing as a result of digitalization and IoT advancements. The Internet of Energy (IoE) is developing to link many smart grid components and shareholders effectively. The use of smart meters is becoming more popular in this context. The automatic identification of appliances is one of the most important applications of smart meter data. Enumerated billing and dynamic load management are possible outcomes. This process is complicated due to the usage of many brands and types of equipment. For the purpose of automatically identifying significant home appliances based on their usage patterns, this study presents a novel hybridization of segmentation, time-domain feature extraction, and machine learning algorithms. While automatically categorizing six key household appliances of various manufacturers, the developed technique achieves 96.2 percent accuracy, 97.7 percent specificity, and 98 percent AUC values.