國立高雄大學圖資館 |

登入

回首頁

Artificial intelligence for energy systemsdriving intelligent, flexible and optimal energy management /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence for energy systemsby Elissaios Sarmas, Vangelis Marinakis, Haris Doukas.
其他題名:	driving intelligent, flexible and optimal energy management /
作者:	Sarmas, Elissaios.
其他作者:	Marinakis, Vangelis.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xvii, 266 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Electric power productionTechnological innovations.
電子資源:	https://doi.org/10.1007/978-3-031-85209-1
ISBN:	9783031852091$q(electronic bk.)

Artificial intelligence for energy systemsdriving intelligent, flexible and optimal energy management /
Sarmas, Elissaios.

Artificial intelligence for energy systemsdriving intelligent, flexible and optimal energy management /[electronic resource] :by Elissaios Sarmas, Vangelis Marinakis, Haris Doukas. - Cham :Springer Nature Switzerland :2025. - xvii, 266 p. :ill. (chiefly color), digital ;24 cm. - Learning and analytics in intelligent systems,v. 462662-3455 ;. - Learning and analytics in intelligent systems ;v.1..

1.The Climate Crisis and the Four Pillars of Energy Transition: Decarbonization, Digitization, Decentralization, and Democratization -- 2.The Role of Artificial Intelligence in Transforming the Energy Sector: A Comprehensive Review -- 3.Scalable Framework for Intelligent System Architecture to Address Challenges in the Energy Sector -- 4.Deep Learning Models for Short-Term Forecasting of Photovoltaic Energy Production -- 5.Machine Learning-Driven Energy Consumption Forecasting for Building Profiling -- 6.Meta-Learning Approaches for Assessing Energy Efficiency Investments in Buildings -- 7.Ensemble Machine Learning Models for Estimating Energy Savings from Efficiency Measures in Buildings -- 8.Optimization Model for Scheduling Flexible Loads to Mitigate Energy Peaks -- 9.Optimization Model for Electric Vehicle Integration and Energy Storage to Achieve Energy Autonomy -- 10.Future Directions of Intelligent Energy Management and the Role of Generative AI.

This book focuses on creating an integrated library of learning models and optimization techniques to assist decision-making on issues in the energy and building sector. It provides modern solutions to energy management and efficiency while addressing a scientific gap in the development of advanced algorithmic methods to solve these problems. More specifically, the focus is on the development of models and algorithms for problems falling into three broader categories, namely: (a) Distributed Energy Generation, (b) Microgrid Flexibility, and (c) Building Energy Efficiency. Artificial Intelligence models and mathematical optimization techniques are developed and presented for applications related to each of these categories, through a thorough analysis of the fundamental parameters of each application as well as the interactions among them. Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.

ISBN: 9783031852091$q(electronic bk.)

Standard No.: 10.1007/978-3-031-85209-1doiSubjects--Topical Terms:

247618
Electric power production
--Technological innovations.

LC Class. No.: TK1005

Dewey Class. No.: 621.31

Artificial intelligence for energy systemsdriving intelligent, flexible and optimal energy management /
LDR:03247nmm a2200337 a 4500 001 680335
003 DE-He213
005 20250322115429.0
006 m d
007 cr nn 008maaau
008 251110s2025 sz s 0 eng d
020 $a 9783031852091$q(electronic bk.)
020 $a 9783031852084$q(paper)
024 7 $a 10.1007/978-3-031-85209-1 $2 doi
035 $a 978-3-031-85209-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TK1005
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 621.31 $2 23
090 $a TK1005 $b .S246 2025
100 1 $a Sarmas, Elissaios. $3 880711
245 1 0 $a Artificial intelligence for energy systems $h [electronic resource] : $b driving intelligent, flexible and optimal energy management / $c by Elissaios Sarmas, Vangelis Marinakis, Haris Doukas.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xvii, 266 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Learning and analytics in intelligent systems, $x 2662-3455 ; $v v. 46
505 0 $a 1.The Climate Crisis and the Four Pillars of Energy Transition: Decarbonization, Digitization, Decentralization, and Democratization -- 2.The Role of Artificial Intelligence in Transforming the Energy Sector: A Comprehensive Review -- 3.Scalable Framework for Intelligent System Architecture to Address Challenges in the Energy Sector -- 4.Deep Learning Models for Short-Term Forecasting of Photovoltaic Energy Production -- 5.Machine Learning-Driven Energy Consumption Forecasting for Building Profiling -- 6.Meta-Learning Approaches for Assessing Energy Efficiency Investments in Buildings -- 7.Ensemble Machine Learning Models for Estimating Energy Savings from Efficiency Measures in Buildings -- 8.Optimization Model for Scheduling Flexible Loads to Mitigate Energy Peaks -- 9.Optimization Model for Electric Vehicle Integration and Energy Storage to Achieve Energy Autonomy -- 10.Future Directions of Intelligent Energy Management and the Role of Generative AI.
520 $a This book focuses on creating an integrated library of learning models and optimization techniques to assist decision-making on issues in the energy and building sector. It provides modern solutions to energy management and efficiency while addressing a scientific gap in the development of advanced algorithmic methods to solve these problems. More specifically, the focus is on the development of models and algorithms for problems falling into three broader categories, namely: (a) Distributed Energy Generation, (b) Microgrid Flexibility, and (c) Building Energy Efficiency. Artificial Intelligence models and mathematical optimization techniques are developed and presented for applications related to each of these categories, through a thorough analysis of the fundamental parameters of each application as well as the interactions among them. Professors, researchers, scientists, engineers, and students in energy sector-related disciplines are expected to be inspired and benefit from this book, along with readers from other disciplines wishing to learn more about this exciting new field of research.
650 0 $a Electric power production $x Technological innovations. $3 247618
650 0 $a Electric power $x Conservation. $3 190338
650 0 $a Energy transition. $3 897868
650 0 $a Artificial intelligence $x Engineering applications. $3 559245
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Data Engineering. $3 839346
650 2 4 $a Energy Policy, Economics and Management. $3 511210
650 2 4 $a Artificial Intelligence. $3 212515
700 1 $a Marinakis, Vangelis. $3 975490
700 1 $a Doukas, Haris. $3 837507
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Learning and analytics in intelligent systems ; $v v.1. $3 848241
856 4 0 $u https://doi.org/10.1007/978-3-031-85209-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)