Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach

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

Abstract

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.

Citation

Zhang, J., Sun, J., Gan, J., Liu, Q., & Liu, X. (2021, October). Improving Domestic NILM Using An Attention- Enabled Seq2Point Learning Approach. Presented at The 6th IEEE Cyber Science and Technology Congress (2021) (CyberSciTech 2021), AB, Canada [Online]

• 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
• DOI: https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00079
• Public URL: http://researchrepository.napier.ac.uk/Output/2824655