Sequence-to-Sequence Load Disaggregation Using Multi-Scale Residual Neural Network
This work addresses electricity monitoring efficiency for users, but it is incremental as it builds on existing deep learning methods with specific architectural enhancements.
The paper tackled load disaggregation in non-intrusive load monitoring by proposing a multi-scale residual neural network with dilated convolutions, achieving improvements in F1 score, MAE, and model complexity on the UK-DALE dataset compared to existing neural networks.
With the increased demand on economy and efficiency of measurement technology, Non-Intrusive Load Monitoring (NILM) has received more and more attention as a cost-effective way to monitor electricity and provide feedback to users. Deep neural networks has been shown a great potential in the field of load disaggregation. In this paper, firstly, a new convolutional model based on residual blocks is proposed to avoid the degradation problem which traditional networks more or less suffer from when network layers are increased in order to learn more complex features. Secondly, we propose dilated convolution to curtail the excessive quantity of model parameters and obtain bigger receptive field, and multi-scale structure to learn mixed data features in a more targeted way. Thirdly, we give details about generating training and test set under certain rules. Finally, the algorithm is tested on real-house public dataset, UK-DALE, with three existing neural networks. The results are compared and analysed, the proposed model shows improvements on F1 score, MAE as well as model complexity across different appliances.