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
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 …
Lightweight non-intrusive load monitoring employing pruned sequence-to-point learning
Non-intrusive load monitoring (NILM) is the process in which a household's total power
consumption is used to determine the power consumption of household appliances …
consumption is used to determine the power consumption of household appliances …
LightNILM: lightweight neural network methods for non-intrusive load monitoring
Z Lu, Y Cheng, M Zhong, W Luan, Y Ye… - Proceedings of the 9th …, 2022 - dl.acm.org
The aim of non-intrusive load monitoring (NILM) is to infer the energy consumed by the
appliances in a house given only the total power consumption. Recently, literature have …
appliances in a house given only the total power consumption. Recently, literature have …
MSDC: exploiting multi-state power consumption in non-intrusive load monitoring based on a dual-CNN model
Non-intrusive load monitoring (NILM) aims to decompose aggregated electrical usage
signal into appliance-specific power consumption and it amounts to a classical example of …
signal into appliance-specific power consumption and it amounts to a classical example of …
Combining Smart Speaker and Smart Meter to Infer Your Residential Power Usage by Self-supervised Cross-modal Learning
Energy disaggregation is a key enabling technology for residential power usage monitoring,
which benefits various applications such as carbon emission monitoring and human activity …
which benefits various applications such as carbon emission monitoring and human activity …
Learning to charge RF-energy harvesting devices in WiFi networks
Y Luo, KW Chin - IEEE Systems Journal, 2021 - ieeexplore.ieee.org
Future WiFi networks will be powered by renewable sources. They will also have radio
frequency (RF)-energy harvesting devices. In these networks, a solar-powered access point …
frequency (RF)-energy harvesting devices. In these networks, a solar-powered access point …
[HTML][HTML] Correlation Study Between TV Viewing Variables and Cognitive Level, Depression Level, and Activities of Daily Living in Older Individuals Living Alone
SY Oh, BS Kwon, YG Nam - Healthcare, 2024 - mdpi.com
Background/objectives: Although there are studies on TV viewing and the health status of
elderly, they do not present direct associations with specific variables. The aim of this study …
elderly, they do not present direct associations with specific variables. The aim of this study …
Aiot-empowered smart grid energy management with distributed control and non-intrusive load monitoring
Today's electrical grid is experiencing a fast transition toward a smart infrastructure. Modern
smart grid is expected to integrate Artificial Intelligence of Things (AIoT)-empowered energy …
smart grid is expected to integrate Artificial Intelligence of Things (AIoT)-empowered energy …
A framework to generate and label datasets for non-intrusive load monitoring
In order to reduce the electricity consumption in our homes, a first step is to make the user
aware of it. Raising such awareness, however, demands to pinpoint users of specific …
aware of it. Raising such awareness, however, demands to pinpoint users of specific …