Machine Learning for Energy Systems

Abstract

The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.

Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review

“…The great role of vision and learning of machines, connected by the Internet of Things, in Surface Mining 4.0 makes it possible to move to a deep integration of drilling and blasting, excavation and loading, and transportation processes among themselves, as well as planning and management systems. It gives a chance to optimize the sequence of preparatory, overburdened, and mineral extraction operations in the dangerous and harmful zones performed in unmanned mode [98,99].…”

A study of innovative technologies in the fuel and energy sector

“…Figure 1 shows the WEO-2021 scenario for CO 2 emissions. This review describes which methods should be used to achieve the goal [11]. Hydrogen is gaining popularity in many national and international climate strategies.…”