國立高雄大學圖資館 |

Data science and applications for modern power systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science and applications for modern power systemsby Le Xie, Yang Weng, Ram Rajagopal.
Author:	Xie, Le.
other author:	Weng, Yang.
Published:	Cham :Springer International Publishing :2023.
Description:	xv, 436 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Electric power distributionData processing.
Online resource:	https://doi.org/10.1007/978-3-031-29100-5
ISBN:	9783031291005$q(electronic bk.)

Data science and applications for modern power systems
Xie, Le.

Data science and applications for modern power systems[electronic resource] /by Le Xie, Yang Weng, Ram Rajagopal. - Cham :Springer International Publishing :2023. - xv, 436 p. :ill., digital ;24 cm. - Power electronics and power systems,2196-3193. - Power electronics and power systems..

Big Data Challenges in Power Systems -- Challenges and Opportunities in Utility Data -- Wholesale Markets Data Deluge -- Distribution System Data Operation -- Synchrophasor Data Analytics -- Smart Meter and its Implications -- Deep Learning in Power Markets -- Data-driven Planning in Electric Energy Systems -- Common Information Model for Unifying Data Sets -- Inference and Business for Aggregators Non-intrusive Load Monitoring -- Utility Business Model in the Era of Big Data -- Data Security Services for Utilities.

This book offers a comprehensive collection of research articles that utilize data-in particular large data sets-in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid. Presents a comprehensive review of data sciences for the power industry; Contains state-of-the-art research articles; Provides practical algorithms and case studies.

ISBN: 9783031291005$q(electronic bk.)

Standard No.: 10.1007/978-3-031-29100-5doiSubjects--Topical Terms:

609566
Electric power distribution
--Data processing.

LC Class. No.: TK3091

Dewey Class. No.: 621.319

Data science and applications for modern power systems
LDR:02506nmm a2200337 a 4500 001 646957
003 DE-He213
005 20230620120650.0
006 m d
007 cr nn 008maaau
008 240116s2023 sz s 0 eng d
020 $a 9783031291005$q(electronic bk.)
020 $a 9783031290992$q(paper)
024 7 $a 10.1007/978-3-031-29100-5 $2 doi
035 $a 978-3-031-29100-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TK3091
072 7 $a THR $2 bicssc
072 7 $a TEC046000 $2 bisacsh
072 7 $a THR $2 thema
082 0 4 $a 621.319 $2 23
090 $a TK3091 $b .X6 2023
100 1 $a Xie, Le. $3 956074
245 1 0 $a Data science and applications for modern power systems $h [electronic resource] / $c by Le Xie, Yang Weng, Ram Rajagopal.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xv, 436 p. : $b ill., digital ; $c 24 cm.
490 1 $a Power electronics and power systems, $x 2196-3193
505 0 $a Big Data Challenges in Power Systems -- Challenges and Opportunities in Utility Data -- Wholesale Markets Data Deluge -- Distribution System Data Operation -- Synchrophasor Data Analytics -- Smart Meter and its Implications -- Deep Learning in Power Markets -- Data-driven Planning in Electric Energy Systems -- Common Information Model for Unifying Data Sets -- Inference and Business for Aggregators Non-intrusive Load Monitoring -- Utility Business Model in the Era of Big Data -- Data Security Services for Utilities.
520 $a This book offers a comprehensive collection of research articles that utilize data-in particular large data sets-in modern power systems operation and planning. As the power industry moves towards actively utilizing distributed resources with advanced technologies and incentives, it is becoming increasingly important to benefit from the available heterogeneous data sets for improved decision-making. The authors present a first-of-its-kind comprehensive review of big data opportunities and challenges in the smart grid industry. This book provides succinct and useful theory, practical algorithms, and case studies to improve power grid operations and planning utilizing big data, making it a useful graduate-level reference for students, faculty, and practitioners on the future grid. Presents a comprehensive review of data sciences for the power industry; Contains state-of-the-art research articles; Provides practical algorithms and case studies.
650 0 $a Electric power distribution $x Data processing. $3 609566
650 0 $a Big data. $3 609582
650 1 4 $a Electrical Power Engineering. $3 919410
650 2 4 $a Big Data. $3 760530
650 2 4 $a IT in Business. $3 703717
700 1 $a Weng, Yang. $3 944087
700 1 $a Rajagopal, Ram. $3 956075
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Power electronics and power systems. $3 683055
856 4 0 $u https://doi.org/10.1007/978-3-031-29100-5
950 $a Energy (SpringerNature-40367)