no code implementations • 30 Jun 2022 • Shuang Dai, Fanlin Meng, Qian Wang, Xizhong Chen
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications.
no code implementations • 7 Feb 2022 • Shuang Dai, Fanlin Meng
This survey explored OFL and OTL throughout their major evolutionary routes to enhance understanding of online federated and transfer learning.
no code implementations • 8 Aug 2021 • Shuang Dai, Fanlin Meng, Qian Wang, Xizhong Chen
Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications.
no code implementations • 3 Aug 2021 • Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen
To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature.