Machine learning (ML) models have been widely used in the modeling, design and
prediction in energy systems. During the past two decades, there has been a dramatic …

In alignment with the rapid development of artificial intelligence in the era of data
management, the application domains for machine learning have expanded to all …

Natural gas will play a key role in the transition to a lower-carbon economy, constituting a
natural alternative to coal and acting as a backup resource to the intermittent nature of …

In recent years, due to the deeply concerns on environment protection, the production,
transformation and utilization of the energy sources with a more efficient and various way …

This paper proposes a robust day-ahead scheduling method for a multi-carrier energy
system (MES), which would enhance the flexibility of power systems with a large sum of …

Huge amounts of data are circulating in the digital world in the era of the Industry 5.0
revolution. Machine learning is experiencing success in several sectors such as intelligent …

Monitoring and controlling energy use is critical for efficient power system management,
particularly in smart grids. The internet of things (IoT) has compelled the development of …

This article presents a robust chance-constrained optimization framework for the optimal
operation management of an energy hub (EH) in the presence of electrical, heating, and …

Many researches have investigated the optimal energy scheduling to meet the demand
regarding the sustainable development issues and the economic and environmental …

In this paper, the multi carrier energy (MCE) systems are reviewed from different point of
views including mathematical models, integrated components and technologies, uncertainty …