The emergence of Explainable Artificial Intelligence (XAI) has enhanced the lives of humans
and envisioned the concept of smart cities using informed actions, enhanced user …

The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …

Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-
type data about the electric power grid operations, by integrating advanced metering …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Modern distribution grids are currently being challenged by frequent and sizable voltage
fluctuations, due mainly to the increasing deployment of electric vehicles and renewable …

This article reviews recent works applying machine learning (ML) techniques in the context
of energy systems' reliability assessment and control. We showcase both the progress …

The use of machine learning and data-driven methods for predictive analysis of power
systems offers the potential to accurately predict and manage the behavior of these systems …

Optimization theory assisted algorithms have received great attention for precoding design
in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant …