Deyvison de Paiva Penha and Adriana Rosa Garcez Castro, Federal University of Para, Brazil

This paper presents the proposal of A new methodology for the identification of residential equipment in non-intrusive load monitoring systems that is based on a Convolutional Neural Network to classify equipment. The transient power signal data obtained at the time an equipment is connected in a residence is used as inputs to the system. 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 indicate that the proposed system is able to carry out the identification task, and presented satisfactory results when compared with the results already presented in the literature for the problem in question.

Convolutional Neural Networks, Identification of Residential Equipment, Non-Intrusive Load Monitoring, NILM System, Energy Conservation