The energy industry is at a crossroads. Digital technological developments have the
potential to change our energy supply, trade, and consumption dramatically. The new …

Abstract Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and
regression tasks in Big Data. Researchers should be familiar with the strengths and …

Industry 4.0 is the new industrial revolution. By connecting every machine and activity
through network sensors to the Internet, a huge amount of data is generated. Machine …

Cyber-physical attacks are the main substantial threats facing the utilization and
development of the various smart grid technologies. Among these attacks, false data …

Accurate and rapid load forecasting for industrial customers has been playing a crucial role
in modern power systems. Due to the variability of industrial customers' activities, individual …

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 …

Smart meters are key elements of a smart grid. These data from Smart Meters can help us
analyze energy consumption behaviour. The machine learning and deep learning …

Designing an electricity theft cyberattack detector for the advanced metering infrastructures
(AMIs) is challenging due to the limited availability of electricity theft datasets (ie, malicious …

Metering data from the advanced metering infrastructure can be used to find abnormal
electricity behavior for the detection of electricity theft, which causes huge financial losses to …

This paper presents a novel supervised machine learning-based electric theft detection
approach using the feature engineered-CatBoost algorithm in conjunction with the …