Sequence to point learning based on bidirectional dilated residual network for non-intrusive load monitoring

Non-Intrusive Load Monitoring (NILM) or Energy Disaggregation, seeks to save energy by decomposing corresponding appliances power reading from an aggregate power reading of the whole house. It is regarded as a single channel blind source separation problem to extract sources from a mixed signal. Recent studies have shown that deep learning is widely applied to NILM problem. Theoretically,the ability of any neural network to extract load features is closely related to its depth. However, a deep neural network is difficult to train because of exploding gradient, vanishing gradient, and network degradation. Therefore, Bi-TCN residual block, inspired by a temporal convolution network (TCN), is applied to solve these problems. Causal dilated convolution is replaced by bidirectional(non-causal) dilated convolution to enlarge the receptive field of network and improve the performance of model. Two forms of residual connections are introduced to deep models. One is designed to facilitate training deep models, and the other is pursuing performance-boosting by combining load features extracted of different hierarchical levels to final prediction. We propose a sequence to point learning based on bidirectional dilated convolution for NILM on low-frequency data, called BitcnNILM. We compare our method with existing algorithms on low-frequency data via REDD and UK-DALE datasets. Experiments show that the superiority of our BitcnNILM in both load disaggregation and load on/off identification.

Automated summary

Recent studies have shown that deep learning is widely applied to NILM problem, and the introduction of Bi-TCN residual block and bidirectional dilated convolution can effectively address the training difficulties of deep neural networks. Experiments have demonstrated the superiority of BitcnNILM on low-frequency data.