DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring

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.

Federated Learning Non-Intrusive Load Monitoring +1

Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning

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.

Federated Learning Transfer Learning

FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep 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.

Federated Learning Non-Intrusive Load Monitoring +1

Electrical peak demand forecasting- A review

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.

Management