[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning

Y Himeur, M Elnour, F Fadli, N Meskin, I Petri… - Sustainable Cities and …, 2022 - Elsevier
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …

Deep learning in smart grid technology: A review of recent advancements and future prospects

M Massaoudi, H Abu-Rub, SS Refaat, I Chihi… - IEEE …, 2021 - ieeexplore.ieee.org
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …

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 …

Electric energy disaggregation via non-intrusive load monitoring: A state-of-the-art systematic review

S Dash, NC Sahoo - Electric Power Systems Research, 2022 - Elsevier
Appliance energy consumption tracking in a building is one of the vital enablers of energy
and cost saving. An economical and viable solution would be to estimate individual …

Temporal and spectral feature learning with two-stream convolutional neural networks for appliance recognition in NILM

J Chen, X Wang, X Zhang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Non-intrusive load monitoring (NILM) can monitor the operating state and energy
consumption of appliances without deploying sub-meters and is promising to be widely used …

Deep learning: Systematic review, models, challenges, and research directions

T Talaei Khoei, H Ould Slimane… - Neural Computing and …, 2023 - Springer
The current development in deep learning is witnessing an exponential transition into
automation applications. This automation transition can provide a promising framework for …

Transfer learning for multi-objective non-intrusive load monitoring in smart building

D Li, J Li, X Zeng, V Stankovic, L Stankovic, C Xiao… - Applied Energy, 2023 - Elsevier
Buildings represent 39% of global greenhouse gas emissions, thus reducing carbon
emissions in buildings is of importance to greenhouse gas emissions reductions. This …

Real-time corporate carbon footprint estimation methodology based on appliance identification

G Liu, J Liu, J Zhao, J Qiu, Y Mao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Achieving carbon neutrality is widely recognized as the key measure to mitigate climate
change. As the basis for achieving carbon neutrality, corporate carbon footprint (CCF) …

A novel non-intrusive load monitoring method based on ResNet-seq2seq networks for energy disaggregation of distributed energy resources integrated with …

Y Zhang, W Qian, Y Ye, Y Li, Y Tang, Y Long, M Duan - Applied Energy, 2023 - Elsevier
The increasing effects of global warming and energy depletion have raised concerns about
the pollution caused by traditional oil and fossil energy usage. Distributed energy resources …

DeepDFML-NILM: A new CNN-based architecture for detection, feature extraction and multi-label classification in NILM signals

L da Silva Nolasco, AE Lazzaretti… - IEEE sensors …, 2021 - ieeexplore.ieee.org
In the subsequent decades, the increasing energy will demand renewable resources and
intelligent solutions for managing consumption. In this sense, Non-Intrusive Load Monitoring …