國立高雄大學圖資館 |

Language: English

Back

Energy conversion systems-based artificial intelligenceapplications and tools /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Energy conversion systems-based artificial intelligenceedited by Mahmoud A. Mossa ... [et al.].
Reminder of title:	applications and tools /
other author:	Mossa, Mahmoud A.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xv, 223 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-981-96-2665-6
ISBN:	9789819626656$q(electronic bk.)

Energy conversion systems-based artificial intelligenceapplications and tools /
Energy conversion systems-based artificial intelligenceapplications and tools /[electronic resource] :edited by Mahmoud A. Mossa ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xv, 223 p. :ill., digital ;24 cm. - Energy systems in electrical engineering,2199-8590. - Energy systems in electrical engineering..

Artificial Intelligence Techniques for Energy Conversion Systems -- An Overview of Artificial Intelligence Solutions for the Maintenance and Evaluation of Photovoltaic Systems -- Topologies and Control Technologies of Wind Energy Conversion System -- A Robust DDTC Scheme based on Artificial Neural Networks and Genetic Algorithm for Wind Power Generation -- Intelligent Control of DFIG Based Wind Energy Conversion Systems using Artificial Intelligence Techniques.

This book aims to propose advanced solutions based on artificial intelligence techniques for ECS in order to increase energy efficiency, ensure the safety of the ECS, and to improve the quality of the energy supplied to the grid. The efficiency and quality of the electrical energy produced depends mainly on the structure and efficiency of the control technology developed for the Energy Conversion System (ECS). To improve the performance of ECSs, it is interesting to design control systems that emulate some functions performed by the human brain. Among these interesting functions are self-adaptation, learning, flexibility of operation and planning in the presence of large uncertainties and with minimal information. Based on these aspects, artificial intelligence (AI) techniques can be developed and applied to solve the different control problems of ECSs. For academics, professionals, practitioners, and graduate students interested in the most recent research on the application of AI in ECS, it is the ideal reference source.

ISBN: 9789819626656$q(electronic bk.)

Standard No.: 10.1007/978-981-96-2665-6doiSubjects--Topical Terms:

194058
Artificial intelligence.

LC Class. No.: TA347.A78

Dewey Class. No.: 006.3

Energy conversion systems-based artificial intelligenceapplications and tools /
LDR:02601nmm a22003375a 4500 001 692755
003 DE-He213
005 20250806173951.0
006 m d
007 cr nn 008maaau
008 260622s2025 si s 0 eng d
020 $a 9789819626656$q(electronic bk.)
020 $a 9789819626649$q(paper)
024 7 $a 10.1007/978-981-96-2665-6 $2 doi
035 $a 978-981-96-2665-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TA347.A78
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a TA347.A78 $b E56 2025
245 0 0 $a Energy conversion systems-based artificial intelligence $h [electronic resource] : $b applications and tools / $c edited by Mahmoud A. Mossa ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xv, 223 p. : $b ill., digital ; $c 24 cm.
490 1 $a Energy systems in electrical engineering, $x 2199-8590
505 0 $a Artificial Intelligence Techniques for Energy Conversion Systems -- An Overview of Artificial Intelligence Solutions for the Maintenance and Evaluation of Photovoltaic Systems -- Topologies and Control Technologies of Wind Energy Conversion System -- A Robust DDTC Scheme based on Artificial Neural Networks and Genetic Algorithm for Wind Power Generation -- Intelligent Control of DFIG Based Wind Energy Conversion Systems using Artificial Intelligence Techniques.
520 $a This book aims to propose advanced solutions based on artificial intelligence techniques for ECS in order to increase energy efficiency, ensure the safety of the ECS, and to improve the quality of the energy supplied to the grid. The efficiency and quality of the electrical energy produced depends mainly on the structure and efficiency of the control technology developed for the Energy Conversion System (ECS). To improve the performance of ECSs, it is interesting to design control systems that emulate some functions performed by the human brain. Among these interesting functions are self-adaptation, learning, flexibility of operation and planning in the presence of large uncertainties and with minimal information. Based on these aspects, artificial intelligence (AI) techniques can be developed and applied to solve the different control problems of ECSs. For academics, professionals, practitioners, and graduate students interested in the most recent research on the application of AI in ECS, it is the ideal reference source.
650 0 $a Artificial intelligence. $3 194058
650 0 $a Energy policy. $3 190767
650 0 $a Electric power production. $3 190303
650 1 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Energy Policy, Economics and Management. $3 511210
650 2 4 $a Electrical Power Engineering. $3 919410
700 1 $a Mossa, Mahmoud A. $3 1009188
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Energy systems in electrical engineering. $3 712015
856 4 0 $u https://doi.org/10.1007/978-981-96-2665-6
950 $a Artificial Intelligence (R0) (SpringerNature-85269)