Desagregação de energia baseada em aprendizagem profunda e transformada de wavelet

Abstract

Energy disaggregation is a field which seeks to identifying individual consumption of different appliances using only the aggregated signal measured from a single point. This work proposes a neural network trained with Wavelets reduced data to perform energy disaggregation. Besides the disaggregation, usually a binary answer by identifying the appliance activation moment, we are interested in estimating the appliance’s consumption value. We consider the UK-DALE data set to perform our experiments, containing data from different appliances of five houses from England. Using our strategy, compared with another well-established work, we achieved improvements per appliance of 27.8% (F1-score) in the disaggregation process and 11.4% (estimated accuracy) in the appliance’s consumption value. Our main contribution was to identify satisfactorily that the coefficients of approximation of the Wavelet transform are enough to estimate the individual consumption of household appliances.