Um sistema embarcado para monitoramento de carga não intrusiva usando aprendizado de máquina

• Ano de defesa: 2021
• Autor(a) principal: Braglia, Giovanni
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial; UTFPR
• Palavras-chave em Português: Sistemas embarcados (Computadores); Aprendizado de máquinas; Processamento de sinais; Energia elétrica; Embedded computer systems; Machine learning; Signal processing; Electric power; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/27019

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 project is then completed by real loads’ measurements performed in lab, with the intention of further validate the proposed algorithm and its feasibility, involving the creation of a new load and features dataset. The adopted methodology, its features, drawbacks, and implementation are thus explained, showing current and future challenges for the final application.