A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems.
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| Title: | A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems. |
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| Authors: | Cheng, Lefeng1,2 (AUTHOR) chenglf_scut@163.com, Yu, Tao1,2 (AUTHOR) taoyu1@scut.edu.cn |
| Source: | International Journal of Energy Research. May2019, Vol. 43 Issue 6, p1928-1973. 46p. 20 Diagrams, 2 Charts. |
| Subjects: | Electric power systems, Machine learning, Blended learning, Artificial intelligence, Deep learning, Reinforcement learning |
| Abstract: | Summary: The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Energy Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 136174038 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cheng%2C+Lefeng%22">Cheng, Lefeng</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> chenglf_scut@163.com</i><br /><searchLink fieldCode="AR" term="%22Yu%2C+Tao%22">Yu, Tao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> taoyu1@scut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Energy+Research%22">International Journal of Energy Research</searchLink>. May2019, Vol. 43 Issue 6, p1928-1973. 46p. 20 Diagrams, 2 Charts. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Electric+power+systems%22">Electric power systems</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Blended+learning%22">Blended learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Summary: The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Energy Research is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/er.4333 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 46 StartPage: 1928 Subjects: – SubjectFull: Electric power systems Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Blended learning Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Reinforcement learning Type: general Titles: – TitleFull: A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cheng, Lefeng – PersonEntity: Name: NameFull: Yu, Tao IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 0363907X Numbering: – Type: volume Value: 43 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Energy Research Type: main |
| ResultId | 1 |
