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Recent Innovations in Computing
The chance of growing a framework that may “think” has captivated individuals on account since authentic cases. Man-made consciousness (computer-based intelligence) frameworks contain two preeminent districts, proficient/master frameworks (ES), and engineered/counterfeit neural systems (ANNs). The significant goal of this paper is to show how engineered insight methods may play a basic capacity in demonstrating and forecast of the exhibition of inexhaustible power frameworks. The paper plots know-how of how expert structures and neural systems perform by utilizing way of giving an assortment of issues inside the remarkable controls of inexhaustible force designing. The different utilizations of expert structures and neural systems are given in a topical rather than a sequential or some other request. Results introduced on this paper are declaration to the limit of manufactured knowledge as a plan instrument in heaps of locales of sustainable power engineering.
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1.
go back to reference Ni, Z.: A survey of the development and application of artificial intelligence technology. Coal. Mine Mach. (02), 4–7 (2009) Ni, Z.: A survey of the development and application of artificial intelligence technology. Coal. Mine Mach. (02), 4–7 (2009)
2.
go back to reference Yu, Z.: Artificial intelligence technology development overview. J. Nanjing Univ. Inf. Sci. Technol. 9(03), 297–304 (2017) Yu, Z.: Artificial intelligence technology development overview. J. Nanjing Univ. Inf. Sci. Technol.
9(03), 297–304 (2017)
3.
go back to reference Li, B., Gao, Z.: Application analysis and prospect of artificial intelligence technology in smart grid. Electr. Power 50(12), 136–140 (2017) Li, B., Gao, Z.: Application analysis and prospect of artificial intelligence technology in smart grid. Electr. Power
50(12), 136–140 (2017)
4.
go back to reference Bengio, Y.: Learning deep architectures for AI[J]. Found. Trends in Mach. Learn. (2009) Bengio, Y.: Learning deep architectures for AI[J]. Found. Trends in Mach. Learn. (2009)
5.
go back to reference Wen, S.: Application of Computer Science in Smart Grid. China High Technol. Enterprises 21, 47–49 (2016) Wen, S.: Application of Computer Science in Smart Grid. China High Technol. Enterprises
21, 47–49 (2016)
6.
go back to reference Zhu, C., Yang, J., Chen, S., Luo, Z.: Demand response technology based on artificial intelligence theory in the context of smart grid[J]. Shaanxi Electr. Power 43(07), 63–69 (2015) Zhu, C., Yang, J., Chen, S., Luo, Z.: Demand response technology based on artificial intelligence theory in the context of smart grid[J]. Shaanxi Electr. Power
43(07), 63–69 (2015)
7.
go back to reference Rigas, E.S., Ramchurn, S.D., Bassiliades, N.: Managing electric vehicles in the smart grid using artificial intelligence: a survey. IEEE Trans. Int. Trans. Sys. 16(4), 1619–1635 (2015) CrossRef Rigas, E.S., Ramchurn, S.D., Bassiliades, N.: Managing electric vehicles in the smart grid using artificial intelligence: a survey. IEEE Trans. Int. Trans. Sys.
16(4), 1619–1635 (2015)
CrossRef
- Title
- Artificial Intelligence in the Energy World—Getting the Act Together
- DOI
- https://doi.org/10.1007/978-981-15-8297-4_63
- Authors:
-
Vasundhra Gupta
Rajiv Bali
- Publisher
- Springer Singapore
- Sequence number
- 63