Interpretable Incremental Voltage–Current Representation Attention Convolution Neural Network for Nonintrusive Load Monitoring
Linfei Yin, Chen-Xiao Ma
Abstract
In this article, we propose an interpretable incremental voltage–current representation attention convolution neural network for the nonintrusive load monitoring (NILM) task. The proposed method consists of two parts: First, the voltage–current representation attention mechanism in the proposed network is designed in collaboration with the data preprocessing method. They provide the role for the classification function of neural networks; Second, this article proposes an adaptive distillation incremental learning method that introduced incremental learning into the NILM field. In this work, the public dataset plug-load appliance identification dataset is used to validate the proposed voltage–current representation attention mechanism and adaptive distillation incremental learning method in this article. In addition, the performance of the proposed algorithms is also complemented in this article using a private dataset. According to the experimental results, the performance of the proposed method in this article is better than the comparison methods.