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Energy management = big data in power load forecasting /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Energy management/ Valentin A. Boicea.
其他題名:	big data in power load forecasting /
作者:	Boicea, Valentin A.
出版者:	Boca Raton, FL :CRC Press, : 2021.,
面頁冊數:	1 online resource (1 v.) :ill.
標題:	Power resources - Management. -
電子資源:	https://www.taylorfrancis.com/books/9781003147213
ISBN:	9781003147213

Energy management = big 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: 9781003147213Subjects--Topical Terms:

881350
Power resources
--Management.

LC Class. No.: TJ163.2

Dewey Class. No.: 621.042

Energy management = big data in power load forecasting /
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100 1 $a Boicea, Valentin A. $3 3674526
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 881350
650 0 $a Power resources $x Forecasting. $3 674702
650 0 $a Big data. $3 2045508
856 4 0 $u https://www.taylorfrancis.com/books/9781003147213