[HTML][HTML] Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives

Y Himeur, K Ghanem, A Alsalemi, F Bensaali, A Amira - Applied Energy, 2021 - Elsevier
Enormous amounts of data are being produced everyday by sub-meters and smart sensors
installed in residential buildings. If leveraged properly, that data could assist end-users …

Machine learning driven smart electric power systems: Current trends and new perspectives

MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …

Non-technical losses: A systematic contemporary article review

F de Souza Savian, JCM Siluk, TB Garlet… - … and Sustainable Energy …, 2021 - Elsevier
Non-technical losses refer to all electricity consumption not billed and represent a significant
problem that has consequences to all sectors and a substantial negative impact on some …

Detecting false data attacks using machine learning techniques in smart grid: A survey

L Cui, Y Qu, L Gao, G Xie, S Yu - Journal of Network and Computer …, 2020 - Elsevier
The big data sources in smart grid (SG) enable utilities to monitor, control, and manage the
energy system effectively, which is also promising to advance the efficiency, reliability, and …

Feddetect: A novel privacy-preserving federated learning framework for energy theft detection in smart grid

M Wen, R Xie, K Lu, L Wang… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
In smart grids, a major challenge is how to effectively utilize consumers' energy consumption
data while preserving security and privacy. In this article, we tackle this challenging issue …

Review of the data-driven methods for electricity fraud detection in smart metering systems

MM Badr, MI Ibrahem, HA Kholidy, MM Fouda, M Ismail - Energies, 2023 - mdpi.com
In smart grids, homes are equipped with smart meters (SMs) to monitor electricity
consumption and report fine-grained readings to electric utility companies for billing and …

Reliable industry 4.0 based on machine learning and IOT for analyzing, monitoring, and securing smart meters

M Elsisi, K Mahmoud, M Lehtonen, MMF Darwish - Sensors, 2021 - mdpi.com
The modern control infrastructure that manages and monitors the communication between
the smart machines represents the most effective way to increase the efficiency of the …

Performance analysis of electricity theft detection for the smart grid: An overview

Z Yan, H Wen - IEEE Transactions on Instrumentation and …, 2021 - ieeexplore.ieee.org
Electricity theft has been a growing concern for the smart grid. It can be defined as follows:
illegal customers use energy from electric utilities without a contract or manipulate their …

Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence

A Chehri, I Fofana, X Yang - Sustainability, 2021 - mdpi.com
Smart grids (SG) emerged as a response to the need to modernize the electricity grid. The
current security tools are almost perfect when it comes to identifying and preventing known …

Efficient privacy-preserving electricity theft detection with dynamic billing and load monitoring for AMI networks

MI Ibrahem, M Nabil, MM Fouda… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
In advanced metering infrastructure (AMI), smart meters (SMs) are installed at the consumer
side to send fine-grained power consumption readings periodically to the system operator …