An Empirical Study on Energy Disaggregation via Deep Learning

Energy disaggregation is the task of estimating power consumption of each individual appliance from the whole-house electric signals. In this paper, we study this task based on deep learning methods which have achieved a lot of success in various domains recently. We introduce the feature extraction method that uses multiple parallel convolutional layers of varying filter sizes and present an LSTM (Long Short Term Memory) based recurrent network model as well as an auto-encoder model for energy disaggregation. Then we evaluate the proposed methods using the largest dataset available. And experimental results show the superiority of our feature extraction method and the LSTM based model.