|Tipo di tesi||Tesi di laurea magistrale|
|Titolo||An Embedded System for Non-Intrusive Load Monitoring usign Machine Learning|
|Titolo in inglese|
|Struttura||Dipartimento di Ingegneria "Enzo Ferrari"|
|Corso di studi||ELECTRONICS ENGINEERING - Ingegneria Elettronica (D.M.270/04)|
|Data inizio appello||2021-10-21|
|Disponibilità||Accessibile via web (tutti i file della tesi sono accessibili)|
The interest in power managing systems has been growing in recent years since every industrial or domestic plant moves towards techniques to efficiently reduce energy demand and costs related to it. An attractive solution is represented by Non-Intrusive Load Monitoring (NILM) systems, whose primary purpose is to find a more appropriate way of keeping track of the power consumption caused by each of the loads that are connected to the monitored plant. A possible real-life implementation of a NILM system is addressed in this work, discussing all the fundamental blocks in its structure, including detecting events, feature extraction, and load classification, using publicly available datasets. Additionally, we provide a solution for an embedded system, able to analyze aggregated waveforms and to recognize each appliance's contribution in it. The main algorithm, its features, drawbacks, and implementation are thus explained, showing current and future challenges for the final application.