Artificial intelligence and machine learning in energy systems: A bibliographic perspective
Economic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Energy Strategy Reviews |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X22002115 |
| _version_ | 1797950730667032576 |
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| author | Ashkan Entezari Alireza Aslani Rahim Zahedi Younes Noorollahi |
| author_facet | Ashkan Entezari Alireza Aslani Rahim Zahedi Younes Noorollahi |
| author_sort | Ashkan Entezari |
| collection | DOAJ |
| description | Economic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems, such a feat is by no means easy. Here is where computer-aided decision-making can be very game-changing in determining the optimum point for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans' learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential areas in many different research fields. Naturally, the energy section is one area where artificial intelligence and machine learning can be very beneficial. This paper uses the VOSviewer software to investigate and review the usage of artificial intelligence and machine learning in the energy field and proposes promising yet neglected or unexplored areas in which these concepts can be used. To achieve this, the 2000 most recent papers in addition to the 2000 most cited ones in different energy-related keywords were studied and their relationship to AI- and ML-related keywords was visualized. The results revealed different research trends in recent years from the basic to more cutting-edge topics and revealed many promising areas that are yet to be explored. Results also showed that from the commercial aspect, patents submitted for artificial intelligence and machine learning in energy-related areas had a sharp increase. |
| first_indexed | 2024-04-10T22:19:46Z |
| format | Article |
| id | doaj.art-5e9947ba35cf4e32a8d075f58e03919e |
| institution | Directory Open Access Journal |
| issn | 2211-467X |
| language | English |
| last_indexed | 2024-04-10T22:19:46Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj.art-5e9947ba35cf4e32a8d075f58e03919e2023-01-18T04:30:56ZengElsevierEnergy Strategy Reviews2211-467X2023-01-0145101017Artificial intelligence and machine learning in energy systems: A bibliographic perspectiveAshkan Entezari0Alireza Aslani1Rahim Zahedi2Younes Noorollahi3Faculty of New Science and Technologies, University of Tehran, Tehran, IranCorresponding author.; Faculty of New Science and Technologies, University of Tehran, Tehran, IranFaculty of New Science and Technologies, University of Tehran, Tehran, IranCorresponding author. University of Tehran, Iran.; Faculty of New Science and Technologies, University of Tehran, Tehran, IranEconomic development and the comfort-loving nature of human beings in recent years have resulted in increased energy demand. Since energy resources are scarce and should be preserved for future generations, optimizing energy systems is ideal. Still, due to the complexity of integrated energy systems, such a feat is by no means easy. Here is where computer-aided decision-making can be very game-changing in determining the optimum point for supply and demand. The concept of artificial intelligence (AI) and machine learning (ML) was born in the twentieth century to enable computers to simulate humans' learning and decision-making capabilities. Since then, data mining and artificial intelligence have become increasingly essential areas in many different research fields. Naturally, the energy section is one area where artificial intelligence and machine learning can be very beneficial. This paper uses the VOSviewer software to investigate and review the usage of artificial intelligence and machine learning in the energy field and proposes promising yet neglected or unexplored areas in which these concepts can be used. To achieve this, the 2000 most recent papers in addition to the 2000 most cited ones in different energy-related keywords were studied and their relationship to AI- and ML-related keywords was visualized. The results revealed different research trends in recent years from the basic to more cutting-edge topics and revealed many promising areas that are yet to be explored. Results also showed that from the commercial aspect, patents submitted for artificial intelligence and machine learning in energy-related areas had a sharp increase.http://www.sciencedirect.com/science/article/pii/S2211467X22002115Artificial intelligenceMachine learningEnergy systemsBibliographic research |
| spellingShingle | Ashkan Entezari Alireza Aslani Rahim Zahedi Younes Noorollahi Artificial intelligence and machine learning in energy systems: A bibliographic perspective Energy Strategy Reviews Artificial intelligence Machine learning Energy systems Bibliographic research |
| title | Artificial intelligence and machine learning in energy systems: A bibliographic perspective |
| title_full | Artificial intelligence and machine learning in energy systems: A bibliographic perspective |
| title_fullStr | Artificial intelligence and machine learning in energy systems: A bibliographic perspective |
| title_full_unstemmed | Artificial intelligence and machine learning in energy systems: A bibliographic perspective |
| title_short | Artificial intelligence and machine learning in energy systems: A bibliographic perspective |
| title_sort | artificial intelligence and machine learning in energy systems a bibliographic perspective |
| topic | Artificial intelligence Machine learning Energy systems Bibliographic research |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X22002115 |
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