Analysis of Electrical Signals by Machine Learning for Classification of Individualized Electronics on the Internet of Smart Grid Things (IoSGT) architecture

This paper presents a study on the performance of Machine Learning techniques for the task of determining each of the electronics systems connected within the same electrical network, in the Internet of Smart Grid Things (IoSGT) architecture. This is regarded as an ecosystem which has cutting-edge technologies that work together to enable advanced SG applications, and run in core/edge cloud datacenters connected to an underlying IoT network infrastructure. This identification was carried out by analyzing traditional energy measures (i.e., voltage, current and power) through ML-based techniques. The analysis of regular energy measurements is required as a means of ensuring compatibility with legacy /smart meters, without the need to exchange them for new ones or make updates. The data was obtained by using a smart meter built by our group, and was processed and validated in the IoSGT edge-cloud.