Electrical devices identification driven by features and based on machine learning

Abstract

The analysis of energy data of electrical devices in Smart Homes (SHs) represents an important factor for the decision-making process of energy management both from the consumer perspective by saving money and also in terms of energy redistribution and CO $$:{2}$$ emissions reduction, by knowing how the energy demand of a building is composed in the Smart Grid (SG). A proactive monitoring and control mechanism motivates the need to face with the identification of appliances. In this context, the paper proposes a model for the automatic identification of electrical devices driven by 19 features that are formalized through a mathematical notation. On the basis of such proposed features, three different classifiers are trained and experimented, by evaluating their accuracy, for the identification of 33 types of appliances.