Energy management big data in power load forecasting / Valentin A. Boicea.

Author/creator Boicea, Valentin A.
Format Electronic
EditionFirst edition.
Publication InfoBoca Raton, FL : CRC Press, 2021.
Description1 online resource (1 volume) : illustrations (black and white)
Supplemental ContentFull text available from eBooks on EBSCOhost
Supplemental ContentFull text available from Taylor & Francis eBooks
Subjects

Abstract 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.
Bibliography noteIncludes bibliographical references and index.
Access restrictionAvailable only to authorized users.
Technical detailsMode of access: World Wide Web
Biographical noteAdrian-Valentin Boicea, a former PhD student at Politecnico di Torino, Italy, received the BS in electrical engineering and electrical power systems from the University Politehnica of Bucharest (UPB), Romania. Currently, he is a Lecturer within the Department of Electrical Power Systems at the UPB. His research interests include the distributed generation systems, energy efficiency, renewable sources, the operational research algorithms used in power engineering, as well as Big Data analysis applied in the energy sector.
Source of descriptionPrint version record.
Issued in other formPrint version: BOICEA, VALENTIN A. ENERGY MANAGEMENT. [Place of publication not identified] : CRC PRESS, 2021 0367706164
Genre/formElectronic books.
LCCN 2021762291
ISBN9781000437683 (electronic bk.)
ISBN100043768X (electronic bk.)
ISBN9781003147213 (electronic bk.)
ISBN1003147216 (electronic bk.)
ISBN9781000437812 (electronic bk. ; EPUB)
ISBN1000437817 (electronic bk. ; EPUB)
Standard identifier# 10.1201/9781003147213
Stock number9781003147213 Taylor & Francis

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