Convolutional sequence to sequence non‐intrusive load monitoring

作者：Kunjin Chen, Kunjin Chen, Qin Wang, Ziyu He, Kunlong Chen, Kunlong Chen, Jun Hu, Jinliang He · 发表于：The Journal of Engineering · 年份：2018 · DOI：10.1049/joe.2018.8352 · 被引用次数：105 · 研究领域：Smart Grid Energy Management、Energy Load and Power Forecasting、Building Energy and Comfort Optimization

A convolutional sequence to sequence non‐intrusive load monitoring model is proposed in this study. 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. The authors apply the proposed model to the reference energy disaggregation data set 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.