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 …
Electric energy disaggregation via non-intrusive load monitoring: A state-of-the-art systematic review
S Dash, NC Sahoo - Electric Power Systems Research, 2022 - Elsevier
Appliance energy consumption tracking in a building is one of the vital enablers of energy
and cost saving. An economical and viable solution would be to estimate individual …
and cost saving. An economical and viable solution would be to estimate individual …
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 review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context
The rising demand for energy conservation in residential buildings has increased interest in
load monitoring techniques by exploiting energy consumption data. In recent years …
load monitoring techniques by exploiting energy consumption data. In recent years …
[HTML][HTML] Leveraging sequence-to-sequence learning for online non-intrusive load monitoring in edge device
Non-intrusive load monitoring (NILM), extracting the appliances' usage profiles by
decomposing a household's aggregate electricity consumption, has become increasingly …
decomposing a household's aggregate electricity consumption, has become increasingly …
Performance-aware NILM model optimization for edge deployment
Non-Intrusive Load Monitoring (NILM) describes the extraction of the individual consumption
pattern of a domestic appliance from the aggregated household consumption. Nowadays …
pattern of a domestic appliance from the aggregated household consumption. Nowadays …
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 …
SAED: Self-attentive energy disaggregation
The field of energy disaggregation deals with the approximation of appliance electric
consumption using only the aggregate consumption measurement of a mains meter. Recent …
consumption using only the aggregate consumption measurement of a mains meter. Recent …