Blockchain and machine learning for future smart grids: A review
Developments such as the increasing electrical energy demand, growth of renewable
energy sources, cyber–physical security threats, increased penetration of electric vehicles …
energy sources, cyber–physical security threats, increased penetration of electric vehicles …
A systematic literature review and future perspectives for handling big data analytics in COVID-19 diagnosis
In today's digital world, information is growing along with the expansion of Internet usage
worldwide. As a consequence, bulk of data is generated constantly which is known to be …
worldwide. As a consequence, bulk of data is generated constantly which is known to be …
A stacked machine and deep learning-based approach for analysing electricity theft in smart grids
The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids.
However, existing methods for theft detection can struggle to handle large electricity …
However, existing methods for theft detection can struggle to handle large electricity …
Alexnet, adaboost and artificial bee colony based hybrid model for electricity theft detection in smart grids
Electricity theft (ET) is an utmost problem for power utilities because it threatens public
safety, disturbs the normal working of grid infrastructure and increases revenue losses. In …
safety, disturbs the normal working of grid infrastructure and increases revenue losses. In …
Electricity theft detection using Euclidean and graph convolutional neural networks
The widespread penetration of advanced metering infrastructure brings an opportunity to
detect electricity theft by analyzing the electricity consumption data collected from smart …
detect electricity theft by analyzing the electricity consumption data collected from smart …
Electricity theft detection using big data and genetic algorithm in electric power systems
Non-technical losses (NTLs) are one of the major causes of revenue losses for electric
utilities. In the literature, various machine learning (ML)/deep learning (DL) approaches are …
utilities. In the literature, various machine learning (ML)/deep learning (DL) approaches are …
Robust data driven analysis for electricity theft attack-resilient power grid
The role of electricity theft detection (ETD) is critical to maintain cost-efficiency in smart grids.
However, existing ETD methods cannot efficiently handle the sheer volume of data now …
However, existing ETD methods cannot efficiently handle the sheer volume of data now …
Synthetic theft attacks and long short term memory-based preprocessing for electricity theft detection using gated recurrent unit
Electricity theft is one of the challenging problems in smart grids. The power utilities around
the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) …
the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) …
A CNN-transformer hybrid approach for an intrusion detection system in advanced metering infrastructure
R Yao, N Wang, P Chen, D Ma, X Sheng - Multimedia Tools and …, 2023 - Springer
Bi-directional communication networks are the foundation of advanced metering
infrastructure (AMI), but they also expose smart grids to serious intrusion risks. While …
infrastructure (AMI), but they also expose smart grids to serious intrusion risks. While …
[HTML][HTML] A systematic review of big data innovations in smart grids
H Taherdoost - Results in Engineering, 2024 - Elsevier
Multiple industries have been revolutionized by the incorporation of data science
advancements into intelligent environment technologies, specifically in the context of smart …
advancements into intelligent environment technologies, specifically in the context of smart …