Skrobek, D;
Krzywanski, J;
Sosnowski, M;
Uddin, GM;
Ashraf, WM;
Grabowska, K;
Zylka, A;
... Nowak, W; + view all
(2023)
Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives.
Energies
, 16
(8)
, Article 3441. 10.3390/en16083441.
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Abstract
In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940–2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artificial intelligence in energy processes and systems. The article also discusses the possible directions for the future development of neural networks.
| Type: | Article |
|---|---|
| Title: | Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/en16083441 |
| Publisher version: | https://doi.org/10.3390/en16083441 |
| Language: | English |
| Additional information: | Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). |
| Keywords: | artificial intelligence; neural networks; machine learning; deep learning; energy processes and systems |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10169994 |
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