2018-06-252018-06-252018-04-03PENHA, Deyvison de Paiva. Rede neural convolucional aplicada à identificação de equipamentos residenciais para sistemas de monitoramento não-intrusivo de carga. Orientadora: Adriana Rosa Garcez Castro. 2018. 55 f. Dissertação (Mestrado em Engenharia Elétrica) - Belém, 2018. Disponível em: http://repositorio.ufpa.br/jspui/handle/2011/10063. Acesso em:.https://repositorio.ufpa.br/handle/2011/10063This research presents the proposal of a new methodology for the identification of residential equipment in non-intrusive load monitoring systems. The system is based on a Convolutional Neural Network to classify residential equipment, which uses directly as inputs to the system, the transient power signal data of 7 equipment obtained at the moment they are connected in a residence. The methodology was developed using data from a public database (REED) that presents data collected at a low frequency (1 Hz). The results obtained in the test database show an accuracy of more than 90%, indicating that the proposed system is capable of performing the task of identification. In addition, the results presented are considered satisfactory when compared with the results already presented in the literature for the problem in question.Acesso AbertoRedes neurais convolucionaisIdentificação de Equipamentos ResidenciaisMonitoramento Não-Intrusivo de CargasNon- Intrusive Load Monitoring (NILM)Convolutional neural networksIdentification of Residential EquipmentDissertaçãoCNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::SISTEMAS ELETRICOS DE POTENCIA::TRANSMISSAO DA ENERGIA ELETRICA, DISTRIBUICAO DA ENERGIA ELETRICAINTELIGÊNCIA COMPUTACIONALCOMPUTAÇÃO APLICADA