As the global population grows, and urbanization becomes more prevalent, cities often
struggle to provide convenient, secure, and sustainable lifestyles due to the lack of …

This work reviews maintenance optimization from different and complementary points of
view. Specifically, we systematically analyze the knowledge, information and data that can …

Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …

This paper presents a problem in power networks that creates an exciting and yet
challenging real-world scenario for application of multi-agent reinforcement learning …

With large-scale integration of renewable generation and distributed energy resources,
modern power systems are confronted with new operational challenges, such as growing …

Deep neural networks have revolutionized many machine learning tasks in power systems,
ranging from pattern recognition to signal processing. The data in these tasks are typically …

This article presents a review of current advances and prospects in the field of forecasting
renewable energy generation using machine learning (ML) and deep learning (DL) …

In recent years, digitalisation has rendered machine learning a key tool for improving
processes in several sectors, as in the case of electrical power systems. Machine learning …

Security-constrained unit commitment (SCUC) has been extensively studied as a key
decision-making tool to determine optimal power generation schedules in the operation of …

The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …