Classification performance using gated recurrent unit recurrent neural network on energy disaggregation

Abstract

Energy disaggregation or NILM is the best solution to reduce our consumption of electricity. Many algorithms in machine learning are applied to this field. However, the classification results from those algorithms are not as well as expected. In this paper, we propose a new approach to construct a classifier for energy disaggregation with deep learning field. We apply Gated Recurrent Unit (GRU) based on Recurrent Neural Network (RNN) to train our model using UK DALE dataset on this field. Besides, we compare our approach to original RNN on energy disaggregation. By applying GRU RRN, we achieve accuracy and F-measure for energy disaggregation with the ranges [89%-98%] and [81%-98%] respectively. Through these results of the experiment, we confirm that the deep learning approach is really effective for NILM.