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Energy managementbig data in power load forecasting /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Energy managementValentin A. Boicea.
Reminder of title:	big data in power load forecasting /
Author:	Boicea, Valentin A.
Published:	Boca Raton, FL :CRC Press,2021.
Description:	1 online resource (1 v.) :ill.
Subject:	Power resourcesManagement.
Online resource:	https://www.taylorfrancis.com/books/9781003147213
ISBN:	9781003147213$q(ebook)

Energy managementbig data in power load forecasting /
Boicea, Valentin A.

Energy managementbig data in power load forecasting /[electronic resource] :Valentin A. Boicea. - 1st ed. - Boca Raton, FL :CRC Press,2021. - 1 online resource (1 v.) :ill.

Includes bibliographical references and index.

This book introduces the principle of carrying out a medium-term load forecast (MTLF) at power system level, based on the Big Data concept and Convolutionary Neural Network (CNNs). It also presents further research directions in the field of Deep Learning techniques and Big Data, as well as how these two concepts are used in power engineering. Efficient processing and accuracy of Big Data in the load forecast in power engineering leads to a significant improvement in the consumption pattern of the client and, implicitly, a better consumer awareness. At the same time, new energy services and new lines of business can be developed. The book will be of interest to electrical engineers, power engineers, and energy services professionals.

ISBN: 9781003147213$q(ebook)Subjects--Topical Terms:

724862
Power resources
--Management.

LC Class. No.: TJ163.2

Dewey Class. No.: 621.042

Energy managementbig data in power load forecasting /
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100 1 $a Boicea, Valentin A. $3 960327
245 1 0 $a Energy management $h [electronic resource] : $b big data in power load forecasting / $c Valentin A. Boicea.
250 $a 1st ed.
260 $a Boca Raton, FL : $b CRC Press, $c 2021.
300 $a 1 online resource (1 v.) : $b ill.
504 $a Includes bibliographical references and index.
520 $a This book introduces the principle of carrying out a medium-term load forecast (MTLF) at power system level, based on the Big Data concept and Convolutionary Neural Network (CNNs). It also presents further research directions in the field of Deep Learning techniques and Big Data, as well as how these two concepts are used in power engineering. Efficient processing and accuracy of Big Data in the load forecast in power engineering leads to a significant improvement in the consumption pattern of the client and, implicitly, a better consumer awareness. At the same time, new energy services and new lines of business can be developed. The book will be of interest to electrical engineers, power engineers, and energy services professionals.
588 $a Description based on print version record.
650 0 $a Power resources $x Management. $3 724862
650 0 $a Power resources $x Forecasting. $3 341094
650 0 $a Big data. $3 609582
856 4 0 $u https://www.taylorfrancis.com/books/9781003147213