Development of methods and algorithms for identification of a type of electric energy consumers using artificial intelligence and machine learning models for Smart Grid Systems

Abstract

Abstract Article presents the relevance of creating Smart Grid for public power networks and industrial enterprises. The article describes an experiment in which data were collected from current and voltage sensors from several different consumers of electric energy, using the created Smart Socket - Smart Energy device. The methods and algorithms of intellectual and spectral analysis were used to analyze and process experimental data. In this paper, we developed an algorithm for identifying the type of consumer of electric energy in the network of general power supply using the machine learning model XGBoost - extreme gradient boosted decision trees.