Sequence-to-Sequence Load Disaggregation Using Multiscale Residual Neural Network

doi:10.1109/TIM.2020.3034989

Sequence-to-Sequence Load Disaggregation Using Multiscale Residual Neural Network

2021-01-01

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DOI：10.1109/TIM.2020.3034989

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With the increased demand on economy and efficiency of measurement technology, nonintrusive 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 have been showing a great potential in the field of load disaggregation. In this article, first, a new convolutional model based on residual blocks is proposed to avoid the degradation problem whose traditional networks more or less suffer from when network layers are increased in order to learn more complex features. Second, we propose dilated convolution to curtail the excessive quantity of model parameters and obtain bigger receptive field and multiscale structure to learn mixed data features in a more targeted way. Third, we give details about generating training and test set under certain rules. Finally, the algorithm is tested on real-house public data set, UK Domestic Application Level Electric (UK-DALE), with three existing neural networks. The results are compared and analyzed, and the proposed model shows improvements on F1 score, MAE, as well as model complexity across different appliances.

Keywords:

Load disaggregation

model complexity

multiscale

nonintrusive load monitoring (NILM)

residual neural network

Sequence-to-Sequence Load Disaggregation Using Multiscale Residual Neural Network

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