Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach

Research Output

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.

Date:

15 March 2022
Publication Status:

Published
DOI:

10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00079
Funders:

RSE Royal Society of Edinburgh

http://researchrepository.napier.ac.uk/output/2824655 <p>Zhang, J., Sun, J., Gan, J., Liu, Q., & Liu, X. (2022). Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach. In <i>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)</i>. https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00079</p>

Citation

Zhang, J., Sun, J., Gan, J., Liu, Q., & Liu, X. (2022). Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach. In 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). https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00079