Machine Learning and Clustering techniques for Anomalies Detection in Household Appliances

Riccardo Sappa

Machine Learning and Clustering techniques for Anomalies Detection in Household Appliances.

Rel. Edoardo Patti, Mulugeta Weldezgina Asres, Marco Castangia. Politecnico di Torino, Master of science program in Ict For Smart Societies, 2021

Abstract

The incredible growth of the Smart Grids technology during the last 5 to 10 years has seen in parallel the need to have technologies capable of improving the user experience. The user has gained new control over his devices, but he has yet to obtain the right awareness, for example of his consumption. One of the leading sectors in which the use of technology can improve the user experience is, in fact, in the appliances consumption and behaviours. This project aims at creating a framework placed in this environment, specifically focused on the task of detecting and analysing anomalies in the household appliances.

An anomaly is an abnormal event which differs significantly from the normal behaviour of the data set in which it occurs