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Electricity theft and non-technical losses (NTLs) remain critical challenges in modern smart grids, causing significant economic losses and compromising grid reliability. This study introduces the SmartGuard Energy Intelligence System…

Artificial intelligence-based techniques applied to the electricity consumption data generated from the smart grid prove to be an effective solution in reducing Non Technical Loses (NTLs), thereby ensures safety, reliability, and security…

Machine Learning · Computer Science 2021-10-12 Yogesh Kulkarni , Sayf Hussain Z , Krithi Ramamritham , Nivethitha Somu

Non-technical losses (NTL) in electric power grids arise through electricity theft, broken electric meters or billing errors. They can harm the power supplier as well as the whole economy of a country through losses of up to 40% of the…

Cryptography and Security · Computer Science 2018-04-17 Niklas Dahringer

In order to keep track of the operational state of power grid, the world's largest sensor systems, smart grid, was built by deploying hundreds of millions of smart meters. Such system makes it possible to discover and make quick response to…

Machine Learning · Computer Science 2019-07-10 Jiangteng Li , Fei Wang

With the increasing integration of smart meters in electrical grids worldwide, detecting energy theft has become a critical and ongoing challenge. Artificial intelligence (AI)-based models have demonstrated strong performance in identifying…

Machine Learning · Computer Science 2025-07-08 Caylum Collier , Krishnendu Guha

Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity distributed in emerging countries. Industrial NTL detection…

Purpose: This paper aims to enhance bearing fault diagnosis in industrial machinery by introducing a novel method that combines Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) networks. This approach captures both spatial…

This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of the system through the…

Machine Learning · Computer Science 2023-03-28 Seyed Hamed Haghshenas , Md Abul Hasnat , Mia Naeini

Electricity theft is a major problem around the world in both developed and developing countries and may range up to 40% of the total electricity distributed. More generally, electricity theft belongs to non-technical losses (NTL), which…

Machine Learning · Computer Science 2017-07-26 Patrick Glauner , Jorge Meira , Lautaro Dolberg , Radu State , Franck Bettinger , Yves Rangoni , Diogo Duarte

Accurate power load forecasting is essential for the efficient operation and planning of electrical grids, particularly given the increased variability and complexity introduced by renewable energy sources. This paper introduces GAT-LSTM, a…

Machine Learning · Computer Science 2025-02-13 Ugochukwu Orji , Çiçek Güven , Dan Stowell

Smart grid infrastructure needs improved resilience and preventive maintenance through more accurate predictions. Current methodologies lack accurate representation of spatio-temporal-causal interdependencies and class imbalance in failure…

Systems and Control · Electrical Eng. & Systems 2026-01-07 Anh Le , Phat K. Huynh , Om P. Yadav , Harun Pirim , Chau Le , Trung Q. Le

Smart grids extremely rely on Information and Communications Technology (ICT) and smart meters to control and manage numerous parameters of the network. However, using these infrastructures make smart grids more vulnerable to cyber threats…

Machine Learning · Computer Science 2021-02-12 Hossein Mohammadi Rouzbahani , Hadis Karimipour , Lei Lei

Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate…

Machine Learning · Computer Science 2017-07-26 Patrick O. Glauner , Andre Boechat , Lautaro Dolberg , Radu State , Franck Bettinger , Yves Rangoni , Diogo Duarte

The evolution of the traditional power grid into the "smart grid" has resulted in a fundamental shift in energy management, which allows the integration of renewable energy sources with modern communication technology. However, this…

Artificial Intelligence · Computer Science 2025-09-10 Abdulhakim Alsaiari , Mohammad Ilyas

Spatio-temporal graph neural networks (ST-GNNs) have achieved notable success in structured domains such as road traffic and public transportation, where spatial entities can be naturally represented as fixed nodes. In contrast, many…

Machine Learning · Computer Science 2025-12-24 Jeehong Kim , Youngseok Hwang , Minchan Kim , Sungho Bae , Hyunwoo Park

The modern power grid is facing increasing complexities, primarily stemming from the integration of renewable energy sources and evolving consumption patterns. This paper introduces an innovative methodology that harnesses Convolutional…

Machine Learning · Computer Science 2023-10-26 Aneesh Sathe , Wen Ren Yang

Fault location in distribution grids is critical for reliability and minimizing outage durations. Yet, it remains challenging due to partial observability, given sparse measurement infrastructure. Recent works show promising results by…

Machine Learning · Computer Science 2026-05-11 Burak Karabulut , Carlo Manna , Chris Develder

Modern smart grids rely on advanced metering infrastructure (AMI) networks for monitoring and billing purposes. However, such an approach suffers from electricity theft cyberattacks. Different from the existing research that utilizes…

Machine Learning · Computer Science 2018-09-10 Mahmoud Nabil , Muhammad Ismail , Mohamed Mahmoud , Mostafa Shahin , Khalid Qaraqe , Erchin Serpedin

Precise and timely fault diagnosis is a prerequisite for a distribution system to ensure minimum downtime and maintain reliable operation. This necessitates access to a comprehensive procedure that can provide the grid operators with…

Artificial Intelligence · Computer Science 2024-09-11 Dibaloke Chanda , Nasim Yahya Soltani

An important tool grid operators use to safeguard against failures, whether naturally occurring or malicious, involves detecting anomalies in the power system SCADA data. In this paper, we aim to solve a real-time anomaly detection problem.…

Machine Learning · Computer Science 2024-04-12 SangWoo Park , Amritanshu Pandey
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