NILM applications: Literature review of learning approaches, recent developments and challenges
This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem,
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …
Review on deep neural networks applied to low-frequency nilm
P Huber, A Calatroni, A Rumsch, A Paice - Energies, 2021 - mdpi.com
This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep
neural networks to disaggregate appliances from low frequency data, ie, data with sampling …
neural networks to disaggregate appliances from low frequency data, ie, data with sampling …
Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions
Y Himeur, A Alsalemi, F Bensaali… - … Journal of Intelligent …, 2022 - Wiley Online Library
Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for
observing power usage in buildings. It tackles several challenges in transitioning into a more …
observing power usage in buildings. It tackles several challenges in transitioning into a more …
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 …
Neural Fourier energy disaggregation
Deploying energy disaggregation models in the real-world is a challenging task. These
models are usually deep neural networks and can be costly when running on a server or …
models are usually deep neural networks and can be costly when running on a server or …
[HTML][HTML] DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring
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 …
Variational regression for multi-target energy disaggregation
Non-intrusive load monitoring systems that are based on deep learning methods produce
high-accuracy end use detection; however, they are mainly designed with the one vs. one …
high-accuracy end use detection; however, they are mainly designed with the one vs. one …
DiffNILM: a novel framework for non-intrusive load monitoring based on the conditional diffusion model
R Sun, K Dong, J Zhao - Sensors, 2023 - mdpi.com
Non-intrusive Load Monitoring (NILM) is a critical technology that enables detailed analysis
of household energy consumption without requiring individual metering of every appliance …
of household energy consumption without requiring individual metering of every appliance …
Heartdis: a generalizable end-to-end energy disaggregation pipeline
The need for a more energy-efficient future is now more evident than ever. Energy
disagreggation (NILM) methodologies have been proposed as an effective solution for the …
disagreggation (NILM) methodologies have been proposed as an effective solution for the …
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 …