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 …

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 …

Fednilm: Applying federated learning to nilm applications at the edge

Y Zhang, G Tang, Q Huang, Y Wang… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
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 …

EdgeNILM: Towards NILM on edge devices

R Kukunuri, A Aglawe, J Chauhan, K Bhagtani… - Proceedings of the 7th …, 2020 - dl.acm.org
Non-intrusive load monitoring (NILM) or energy disaggregation refers to the task of
estimating the appliance power consumption given the aggregate power consumption …

Time–frequency mask estimation based on deep neural network for flexible load disaggregation in buildings

J Song, Y Lee, E Hwang - IEEE Transactions on Smart Grid, 2021 - ieeexplore.ieee.org
In this paper, a novel mask-based load disaggregation scheme is presented to extract
flexible load profiles in buildings. Flexible loads are those that can be adjusted as needed …

An energy efficient smart metering system using edge computing in LoRa network

P Kumari, R Mishra, HP Gupta, T Dutta… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
An important research issue in smart metering is to correctly transfer the smart meter
readings from consumers to the operator within the given time period by consuming …

Improving the precision of solids velocity measurement in gas-solid fluidized beds with a hybrid machine learning model

H **ao, A Oloruntoba, X Ke, K Gao, L Duan… - Chemical Engineering …, 2024 - Elsevier
Accurate measurement of solids velocity is crucial for fluidized bed reactor design,
optimization, and scaling in chemical engineering. However, the cross-correlation …

Trending machine learning models in cyber‐physical building environment: A survey

Z Hasan, N Roy - Wiley Interdisciplinary Reviews: Data Mining …, 2021 - Wiley Online Library
Electricity usage of buildings (including offices, malls, and residential apartments)
represents a significant portion of a nation's energy expenditure and carbon footprint. In the …

An implementation framework overview of non-intrusive load monitoring

O Al-Khadher, A Mukhtaruddin… - Journal of Sustainable …, 2023 - hrcak.srce.hr
Sažetak The implementation of non-intrusive load monitoring has gained significant
attention as a promising solution for disaggregating and identifying individual appliances' …

An alternative Low-Cost embedded NILM system for household energy conservation with a low sampling rate

S Biansoongnern, B Plangklang - Symmetry, 2022 - mdpi.com
The measurement of the energy consumption of electrical appliances, where the meter is
installed at a single point on the main input circuit of the building, is called non-intrusive load …