Advancing Sustainable IoT Appliance Load Monitoring Through Edge-Enabled Federated Transfer Learning
Non-intrusive Appliance Load Monitoring (NALM) is essential for efficient electricity
consumption tracking in households, promoting eco-friendly practices, and cost reduction …
consumption tracking in households, promoting eco-friendly practices, and cost reduction …
Fednilm: Applying federated learning to nilm applications at the edge
Non-intrusive load monitoring (NILM) helps disaggregate a household's main electricity
consumption to energy usages of individual appliances, greatly cutting down the cost of fine …
consumption to energy usages of individual appliances, greatly cutting down the cost of fine …
A Robust and Privacy-Aware Federated Learning Framework for Non-Intrusive Load Monitoring
With the rollout of smart meters, a vast amount of energy time-series became available from
homes, enabling applications such as non-intrusive load monitoring (NILM). The …
homes, enabling applications such as non-intrusive load monitoring (NILM). The …
Mtfed-nilm: Multi-task federated learning for non-intrusive load monitoring
X Wang, W Li - 2022 IEEE Intl Conf on Dependable, Autonomic …, 2022 - ieeexplore.ieee.org
Non-intrusive Load Monitoring (NILM) models aim to decompose the reading of mains to
identify energy demands and behaviours of the individual appliance for better energy …
identify energy demands and behaviours of the individual appliance for better energy …
Fed-GBM: A cost-effective federated gradient boosting tree for non-intrusive load monitoring
Non-intrusive load monitoring (NILM) is a computational technique to allow appliance-level
energy disaggregation for sustainable energy management. Most NILM models require …
energy disaggregation for sustainable energy management. Most NILM models require …
[HTML][HTML] Perfednilm: a practical personalized federated learning-based non-intrusive load monitoring
Abstract Non-Intrusive Load Monitoring (NILM) is a valuable technique for breaking down
overall power consumption into the energy usage of individual appliances. Understanding …
overall power consumption into the energy usage of individual appliances. Understanding …
A federated learning model with short sequence to point mechanism for smart home energy disaggregation
Residential households contribute significantly to the overall energy consumption in
developed countries. To reduce their energy consumption, they need solutions that help …
developed countries. To reduce their energy consumption, they need solutions that help …
Federatednilm: A distributed and privacy-preserving framework for non-intrusive load monitoring based on federated deep learning
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 …
is effective in disaggregating smart meter readings from the household-level into appliance …
Transferable tree-based ensemble model for non-intrusive load monitoring
Sustainable energy management systems have been increasingly studied in recent years.
Non-intrusive load monitoring (NILM), as a key component, estimates the power …
Non-intrusive load monitoring (NILM), as a key component, estimates the power …
A privacy-preserving distributed energy management framework based on vertical federated learning-based smart data cleaning for smart home electricity data
YH Lin, JC Ciou - Internet of Things, 2024 - Elsevier
Nonintrusive load monitoring (NILM) is a part of home energy management systems ((H)
EMSs), which can advance the systems to leverage and use gathered electricity data (load …
EMSs), which can advance the systems to leverage and use gathered electricity data (load …