AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
In theory, building automation and management systems (BAMSs) can provide all the
components and functionalities required for analyzing and operating buildings. However, in …
components and functionalities required for analyzing and operating buildings. However, in …
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 …
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 …
neural networks to disaggregate appliances from low frequency data, ie, data with sampling …
Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions
Y Himeur, A Alsalemi, F Bensaali… - … Journal of Intelligent …, 2022 - Wiley Online Library
Smart nonintrusive load monitoring (NILM) represents a cost‐efficient technology for
observing power usage in buildings. It tackles several challenges in transitioning into a more …
observing power usage in buildings. It tackles several challenges in transitioning into a more …
Transfer learning for multi-objective non-intrusive load monitoring in smart building
Buildings represent 39% of global greenhouse gas emissions, thus reducing carbon
emissions in buildings is of importance to greenhouse gas emissions reductions. This …
emissions in buildings is of importance to greenhouse gas emissions reductions. This …
Multi-label LSTM autoencoder for non-intrusive appliance load monitoring
This work follows the multi-label classification based paradigm for non-intrusive load
monitoring (NILM). Power consumption signals used for NILM are inherently time varying …
monitoring (NILM). Power consumption signals used for NILM are inherently time varying …
Deep learning-based energy disaggregation and on/off detection of household appliances
Energy disaggregation, aka Non-Intrusive Load Monitoring, aims to separate the energy
consumption of individual appliances from the readings of a mains power meter measuring …
consumption of individual appliances from the readings of a mains power meter measuring …
Multi-label learning for appliance recognition in NILM using Fryze-current decomposition and convolutional neural network
A Faustine, L Pereira - Energies, 2020 - mdpi.com
The advance in energy-sensing and smart-meter technologies have motivated the use of a
Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end …
Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end …
On time series representations for multi-label NILM
C Nalmpantis, D Vrakas - Neural Computing and Applications, 2020 - Springer
Given only the main power consumption of a household, a non-intrusive load monitoring
(NILM) system identifies which appliances are operating. With the rise of Internet of things …
(NILM) system identifies which appliances are operating. With the rise of Internet of things …
[HTML][HTML] Non-intrusive energy estimation using random forest based multi-label classification and integer linear programming
Y Liu, C Liu, Y Shen, X Zhao, S Gao, X Huang - Energy Reports, 2021 - Elsevier
Home energy management system is proposed to reduce the influences caused by the high
ratio penetration of renewable energy generation, through managing and dispatching the …
ratio penetration of renewable energy generation, through managing and dispatching the …