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] 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 …
Unsupervised domain adaptation for nonintrusive load monitoring via adversarial and joint adaptation network
Nonintrusive load monitoring (NILM) is a technique to disaggregate an appliance's load
consumption from the aggregate load in a house. Monitoring the energy behavior has …
consumption from the aggregate load in a house. Monitoring the energy behavior has …
A load identification method based on active deep learning and discrete wavelet transform
L Guo, S Wang, H Chen, Q Shi - IEEE Access, 2020 - ieeexplore.ieee.org
Non-Intrusive Load Monitoring (NILM) makes it possible for users and energy providers to
track the fine-grained energy consumption information of residential and commercial …
track the fine-grained energy consumption information of residential and commercial …
Semi-supervised intrusive appliance load monitoring in smart energy monitoring system
Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption
data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is …
data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is …
Non-intrusive load monitoring system framework and load disaggregation algorithms: a survey
Energy production is challenged by carbon emission reduction and energy conservation at a
time when household demand in energy is in constant evolution. Improving energy …
time when household demand in energy is in constant evolution. Improving energy …
A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid
Non-intrusive load monitoring (NILM) is a technique which extracts individual appliance
consumption and operation state change information from the aggregate power …
consumption and operation state change information from the aggregate power …
[HTML][HTML] Human in the loop active learning for time-series electrical measurement data
Advanced machine learning algorithms require large datasets, along with good-quality
labels to reach state-of-the-art performance. Although measurements themselves can often …
labels to reach state-of-the-art performance. Although measurements themselves can often …
A weakly supervised active learning framework for non-intrusive load monitoring
Energy efficiency is at a critical point now with rising energy prices and decarbonisation of
the residential sector to meet the global NetZero agenda. Non-Intrusive Load Monitoring is a …
the residential sector to meet the global NetZero agenda. Non-Intrusive Load Monitoring is a …
Design of a smart socket functioned with electrical appliance identification
C Yu, P Chen, X Liu, L Zhao, M Han… - 2019 22nd International …, 2019 - ieeexplore.ieee.org
In order to solve a series of problems of the existing smart socket using intrusive load
monitoring method, such as high cost and complicated manufacturing process, this paper …
monitoring method, such as high cost and complicated manufacturing process, this paper …