  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.
