Non-intrusive load monitoring: A review

PA Schirmer, I Mporas - IEEE Transactions on Smart Grid, 2022 - ieeexplore.ieee.org
The rapid development of technology in the electrical energy sector within the last 20 years
has led to growing electric power needs through the increased number of electrical …

NILM applications: Literature review of learning approaches, recent developments and challenges

GF Angelis, C Timplalexis, S Krinidis, D Ioannidis… - Energy and …, 2022 - Elsevier
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 …

Energy management using non-intrusive load monitoring techniques–State-of-the-art and future research directions

R Gopinath, M Kumar, CPC Joshua… - Sustainable Cities and …, 2020 - Elsevier
In recent years, the development of smart sustainable cities has become the primary focus
among urban planners and policy makers to make responsible use of resources, conserve …

A review of current methods and challenges of advanced deep learning-based non-intrusive load monitoring (NILM) in residential context

H Rafiq, P Manandhar, E Rodriguez-Ubinas… - Energy and …, 2024 - Elsevier
The rising demand for energy conservation in residential buildings has increased interest in
load monitoring techniques by exploiting energy consumption data. In recent years …

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 …

[HTML][HTML] NILM techniques for intelligent home energy management and ambient assisted living: A review

A Ruano, A Hernandez, J Ureña, M Ruano, J Garcia - Energies, 2019 - mdpi.com
The ongoing deployment of smart meters and different commercial devices has made
electricity disaggregation feasible in buildings and households, based on a single measure …

Fast and accurate time series classification with weasel

P Schäfer, U Leser - Proceedings of the 2017 ACM on Conference on …, 2017 - dl.acm.org
Time series (TS) occur in many scientific and commercial applications, ranging from earth
surveillance to industry automation to the smart grids. An important type of TS analysis is …

Non-intrusive load monitoring by voltage–current trajectory enabled transfer learning

Y Liu, X Wang, W You - IEEE Transactions on Smart Grid, 2018 - ieeexplore.ieee.org
Non-intrusive load monitoring (NILM) is a technique for analyzing changes in the voltage
and current flowing through the main feeder and determining the appliances in operation as …

A comprehensive review of residential electricity load profile models

E Proedrou - IEEE Access, 2021 - ieeexplore.ieee.org
A third of the final electricity in the EU is consumed by households. The increased usage of
multiple electrical devices, electromobility, self-generation and consumption of electricity as …