東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Machine learning applications for intelligent energy management = invited chapters from experts on the energy field /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning applications for intelligent energy management/ edited by Haris Doukas, Vangelis Marinakis, Elissaios Sarmas.
Reminder of title:	invited chapters from experts on the energy field /
other author:	Doukas, Haris.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xiv, 226 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	AI-Powered Transformation and Decentralization of the Energy Ecosystem -- An Explainable AI-based Framework for Supporting Decisions in Energy Management -- The big data value chain for the provision of AI-enabled energy analytics services -- MODULAR BIG DATA APPLICATIONS FOR ENERGY SERVICES IN BUILDINGS AND DISTRICTS: DIGITAL TWINS, TECHNICAL BUILDING MANAGEMENT SYSTEMS AND ENERGY SAVINGS CALCULATIONS -- Neural network based approaches for fault diagnosis of photovoltaic systems -- Clustering of building stock -- BIG DATA SUPPORTED ANALYTICS FOR NEXT GENERATION ENERGY PERFORMANCE CERTIFICATES -- Synthetic data on buildings.
Contained By:	Springer Nature eBook
Subject:	Electric utilities - ManagementxTechnological innovations. -
Online resource:	https://doi.org/10.1007/978-3-031-47909-0
ISBN:	9783031479090

Machine learning applications for intelligent energy management = invited chapters from experts on the energy field /
Machine learning applications for intelligent energy managementinvited chapters from experts on the energy field /[electronic resource] :edited by Haris Doukas, Vangelis Marinakis, Elissaios Sarmas. - Cham :Springer Nature Switzerland :2024. - xiv, 226 p. :ill. (some col.), digital ;24 cm. - Learning and analytics in intelligent systems,v. 352662-3455 ;. - Learning and analytics in intelligent systems ;v. 35..

AI-Powered Transformation and Decentralization of the Energy Ecosystem -- An Explainable AI-based Framework for Supporting Decisions in Energy Management -- The big data value chain for the provision of AI-enabled energy analytics services -- MODULAR BIG DATA APPLICATIONS FOR ENERGY SERVICES IN BUILDINGS AND DISTRICTS: DIGITAL TWINS, TECHNICAL BUILDING MANAGEMENT SYSTEMS AND ENERGY SAVINGS CALCULATIONS -- Neural network based approaches for fault diagnosis of photovoltaic systems -- Clustering of building stock -- BIG DATA SUPPORTED ANALYTICS FOR NEXT GENERATION ENERGY PERFORMANCE CERTIFICATES -- Synthetic data on buildings.

As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector. The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints. 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: 9783031479090

Standard No.: 10.1007/978-3-031-47909-0doiSubjects--Topical Terms:

3713401
Electric utilities
--ManagementxTechnological innovations.

LC Class. No.: HD9685.A2

Dewey Class. No.: 333.793215

Machine learning applications for intelligent energy management = invited chapters from experts on the energy field /
LDR:02969nmm a2200337 a 4500 001 2369046
003 DE-He213
005 20240127070158.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031479090 $q (electronic bk.)
020 $a 9783031479083 $q (paper)
024 7 $a 10.1007/978-3-031-47909-0 $2 doi
035 $a 978-3-031-47909-0
040 $a GP $c GP
041 0 $a eng
050 4 $a HD9685.A2
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 333.793215 $2 23
090 $a HD9685.A2 $b M149 2024
245 0 0 $a Machine learning applications for intelligent energy management $h [electronic resource] : $b invited chapters from experts on the energy field / $c edited by Haris Doukas, Vangelis Marinakis, Elissaios Sarmas.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xiv, 226 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Learning and analytics in intelligent systems, $x 2662-3455 ; $v v. 35
505 0 $a AI-Powered Transformation and Decentralization of the Energy Ecosystem -- An Explainable AI-based Framework for Supporting Decisions in Energy Management -- The big data value chain for the provision of AI-enabled energy analytics services -- MODULAR BIG DATA APPLICATIONS FOR ENERGY SERVICES IN BUILDINGS AND DISTRICTS: DIGITAL TWINS, TECHNICAL BUILDING MANAGEMENT SYSTEMS AND ENERGY SAVINGS CALCULATIONS -- Neural network based approaches for fault diagnosis of photovoltaic systems -- Clustering of building stock -- BIG DATA SUPPORTED ANALYTICS FOR NEXT GENERATION ENERGY PERFORMANCE CERTIFICATES -- Synthetic data on buildings.
520 $a As carbon dioxide (CO2) emissions and other greenhouse gases constantly rise and constitute the main contributor to climate change, temperature rise and global warming, artificial intelligence, big data, Internet of things, and blockchain technologies are enlisted to help enforce energy transition and transform the entire energy sector. The book at hand presents state-of-the-art developments in artificial intelligence-empowered analytics of energy data and artificial intelligence-empowered application development. Topics covered include a presentation of the various stakeholders in the energy sector and their corresponding required analytic services, such as state-of-the-art machine learning, artificial intelligence, and optimization models and algorithms tailored for a series of demanding energy problems and aiming at providing optimal solutions under specific constraints. 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 utilities $x ManagementxTechnological innovations. $3 3713401
650 0 $a Renewable energy sources. $3 543030
650 0 $a Artificial intelligence $x Industrial applications. $3 653318
650 0 $a Energy transition. $3 3505545
650 0 $a Machine learning. $3 533906
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Electrical and Electronic Engineering. $3 3591890
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Energy Policy, Economics and Management. $3 1532761
700 1 $a Doukas, Haris. $3 3382828
700 1 $a Marinakis, Vangelis. $3 3713399
700 1 $a Sarmas, Elissaios. $3 3527141
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Learning and analytics in intelligent systems ; $v v. 35. $3 3713400
856 4 0 $u https://doi.org/10.1007/978-3-031-47909-0
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)