Energy Disaggregation Based on Deep Learning and Wavelet Transform
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. 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.
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References
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