國立虎尾科技大學 |

Artificial intelligence for energy systems = driving intelligent, flexible and optimal energy management /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Artificial intelligence for energy systems/ by Elissaios Sarmas, Vangelis Marinakis, Haris Doukas.
Reminder of title:	driving intelligent, flexible and optimal energy management /
Author:	Sarmas, Elissaios.
other author:	Marinakis, Vangelis.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xvii, 266 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Electric power production - Technological innovations. -
Online resource:	https://doi.org/10.1007/978-3-031-85209-1
ISBN:	9783031852091

Artificial intelligence for energy systems = driving 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

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

895856
Electric power production
--Technological innovations.

LC Class. No.: TK1005

Dewey Class. No.: 621.31

Artificial intelligence for energy systems = driving intelligent, flexible and optimal energy management /
LDR:03246nam a2200337 a 4500 001 1161431
003 DE-He213
005 20250322115429.0
006 m d
007 cr nn 008maaau
008 251029s2025 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. $e author. $3 1327519
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 : $c 2025. $b Springer Nature Switzerland : $b Imprint: Springer,
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 895856
650 0 $a Electric power $x Conservation. $3 573051
650 0 $a Energy transition. $3 1416854
650 0 $a Artificial intelligence $x Engineering applications. $3 889372
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Data Engineering. $3 1226308
650 2 4 $a Energy Policy, Economics and Management. $3 784769
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Marinakis, Vangelis. $3 1488374
700 1 $a Doukas, Haris. $e editor. $3 1303467
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Learning and analytics in intelligent systems ; $v v.1. $3 1228365
856 4 0 $u https://doi.org/10.1007/978-3-031-85209-1
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)