Dilated residual attention network for load disaggregation

Abstract

Load disaggregation technology is a key technology to realize real-time nonintrusive load monitoring (NILM), and deep learning method has shown great promise for NILM. However, current load disaggregation models based on deep learning are prone to the problems of gradient disappearance and model degradation, and it is difficult to extract effective features from load time series. In order to solve these problems, a new dilated residual attention deep network is proposed for load disaggregation. The proposed model adopts residual learning to extract high-level load features, reduces the difficulty of network optimization and solves the problem of network gradient disappearance. Dilated convolution is introduced to increase the receptive field of convolution kernels, which solves the problem that long-load time-series data are difficult to be learned. Most important of all, the proposed bottom-up and top-down attention mechanism can effectively extract the features of the abrupt points in mains power, improve the accuracy of judging the on/off state of electrical appliances and at the same time improve the learning ability of electrical appliances with low usage. Experiments on WikiEnergy dataset and UK-DALE dataset show that the proposed method achieves more accurate load disaggregation tasks than existing studies, which is of great significance for realizing practical NILM.