Abstract-A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partially overlapped output sequences of the network are averaged to produce the final output of the model. We apply the proposed model to the REDD dataset and compare it with the convolutional sequence to point model in the literature. Results show that the proposed model is able to give satisfactory disaggregation performance for appliances with varied characteristics. Index Terms-Non-intrusive load monitoring, convolutional network, sequence to sequence learning, gated linear unit.
Chen et al. REF proposed a convolutional sequence to sequence model in which gated linear unit convolutional layers were used to extract information from the sequences of aggregate electricity consumption and residual blocks were used to refine the output of the neural network.
Convolutional Sequence to Sequence Non-intrusive Load Monitoring
ArXiv
ArXiv
Computer Science Mathematics