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Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach

Zhang, Jing; Sun, Jiawei; Gan, Jixiang; Liu, Qi; Liu, Xiaodong


Jing Zhang

Jiawei Sun

Jixiang Gan

Qi Liu


The past decade have seen a growth in Internet technology, the overlap of cyberspace and social space provides great convenience for people's life. The in-depth study of non-intrusive load management (NILM) promotes the development of multi-integration and refinement in the future power industry, and makes it possible for customer demand side management. This paper proposes an improved sequence to point load disaggregation algorithm, which combines seq2point learning neural networks with attention mechanism to improve the performance of the algorithm.

Presentation Conference Type Conference Paper (Published)
Conference Name The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021)
Start Date Oct 25, 2021
End Date Oct 28, 2021
Acceptance Date Aug 31, 2021
Online Publication Date Mar 15, 2022
Publication Date 2022
Deposit Date Nov 26, 2021
Publisher Institute of Electrical and Electronics Engineers
Book Title 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
ISBN 978-1-6654-2174-4
Public URL