Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

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 …

This paper introduces for the first time, to our knowledge, a framework for physics-informed
neural networks in power system applications. Exploiting the underlying physical laws …

This paper provides a survey of big data analytics applications and associated
implementation issues. The emphasis is placed on applications that are novel and have …

This paper presents for the first time, to our knowledge, a framework for verifying neural
network behavior in power system applications. Up to this moment, neural networks have …

We propose a neural network architecture that emulates the behavior of a physics solver that
solves electricity differential equations to compute electricity flow in power grids (so-called" …

70In the field of clean water treatment, the use of artificial intelligence is becoming
imperative to achieve two main objectives at present, the control of water quality and the …

Modern power engineering is struggling with various problems that have not been observed
before or have occurred very rarely. The main cause of these problems results from the …

In this article we develop a graph convolutional neural network (GCN) for the approximation
of the AC power flow in electrical power grids. The proposed architecture is fully generic and …

Industrial electric power grid operation subject to an extreme event requires decision making
by human operators under stressful conditions. Decision making using system data …