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 …

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) …

With the increasing penetration of renewable energy and flexible loads in smart grids, a
more complicated power system with high uncertainty is gradually formed, which brings …

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

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 …

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 …

Regenerative braking energy (RBE) utilization plays a vital role in improving the energy
efficiency of electrified railways. To date, various power flow control-based solutions have …