Convolutional sequence to sequence non‐intrusive load monitoring. Issue 17 (31st October 2018)

• Record Type: Journal Article
• Authors: Chen, Kunjin; Wang, Qin; He, Ziyu; Chen, Kunlong; Hu, Jun; He, Jinliang
• Is Part Of: Journal of engineering. Volume 2018:Issue 17(2018)
• Page Start: 1860
• Page End: 1864
• Publication Date: 2018-10-31
• Subjects: power system measurement -- power consumption; gated linear unit convolutional layers -- partially overlapped output sequences -- convolutional sequence to sequence nonintrusive load monitoring model -- information extraction -- electricity consumption -- residual blocks -- reference energy disaggregation data set -- point model; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8352
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : 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.