Towards trustworthy energy disaggregation: A review of challenges, methods, and perspectives for non-intrusive load monitoring
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power
consumption into its individual sub-components. Over the years, signal processing and …
consumption into its individual sub-components. Over the years, signal processing and …
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 …
Non-intrusive load disaggregation using graph signal processing
K He, L Stankovic, J Liao… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
With the large-scale roll-out of smart metering worldwide, there is a growing need to account
for the individual contribution of appliances to the load demand. In this paper, we design a …
for the individual contribution of appliances to the load demand. In this paper, we design a …
[HTML][HTML] An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem
T Todic, V Stankovic, L Stankovic - Applied Energy, 2023 - Elsevier
With the widespread deployment of smart meters worldwide, quantification of energy used
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
by individual appliances via Non-Intrusive Load Monitoring (NILM), ie, virtual submetering, is …
Appliance classification using VI trajectories and convolutional neural networks
Non-intrusive load monitoring methods aim to disaggregate the total power consumption of
a household into individual appliances by analysing changes in the voltage and current …
a household into individual appliances by analysing changes in the voltage and current …
Context aware energy disaggregation using adaptive bidirectional LSTM models
Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), describes various
processes aiming to identify the individual contribution of appliances, given the aggregate …
processes aiming to identify the individual contribution of appliances, given the aggregate …
Bayesian-optimized bidirectional LSTM regression model for non-intrusive load monitoring
In this paper, a Bayesian-optimized bidirectional Long Short-Term Memory (LSTM) method
for energy disaggregation, is introduced. Energy disaggregation, or Non-Intrusive Load …
for energy disaggregation, is introduced. Energy disaggregation, or Non-Intrusive Load …
A survey on non-intrusive load monitoring methodies and techniques for energy disaggregation problem
The rapid urbanization of developing countries coupled with explosion in construction of
high rising buildings and the high power usage in them calls for conservation and efficient …
high rising buildings and the high power usage in them calls for conservation and efficient …
Transferability of neural network approaches for low-rate energy disaggregation
Energy disaggregation of appliances using non-intrusive load monitoring (NILM) represents
a set of signal and information processing methods used for appliance-level information …
a set of signal and information processing methods used for appliance-level information …
[HTML][HTML] Non-intrusive load decomposition based on CNN–LSTM hybrid deep learning model
X Zhou, J Feng, Y Li - Energy Reports, 2021 - Elsevier
With the rapid development of science and technology, the problem of energy load
monitoring and decomposition of electrical equipment has been receiving widespread …
monitoring and decomposition of electrical equipment has been receiving widespread …