This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can …
M Panzer, B Bender - International Journal of Production Research, 2022 - Taylor & Francis
Shortening product development cycles and fully customisable products pose major challenges for production systems. These not only have to cope with an increased product …
J Wang, W Xu, Y Gu, W Song… - Advances in Neural …, 2021 - proceedings.neurips.cc
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 …
W Liao, B Bak-Jensen, JR Pillai… - Journal of Modern …, 2021 - ieeexplore.ieee.org
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 …
N Yang, Z Dong, L Wu, L Zhang, X Shen… - Journal of Modern …, 2021 - ieeexplore.ieee.org
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 …