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A01=Valentin A. Boicea
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Author_Valentin A. Boicea
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Category1=Non-Fiction
Category=PBWH
Category=PHDY
Category=TBM
Category=TH
Category=TJFM
Category=UN
Category=UYQN
Category=UYT
COP=United Kingdom
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Language_English
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Price_€20 to €50
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Energy Management: Big Data in Power Load Forecasting

English

By (author): Valentin A. Boicea

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.

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Current price €24.69
Original price €25.99
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A01=Valentin A. BoiceaAge Group_UncategorizedAuthor_Valentin A. Boiceaautomatic-updateCategory1=Non-FictionCategory=PBWHCategory=PHDYCategory=TBMCategory=THCategory=TJFMCategory=UNCategory=UYQNCategory=UYTCOP=United KingdomDelivery_Pre-orderLanguage_EnglishPA=Not yet availablePrice_€20 to €50PS=Forthcomingsoftlaunch

Will deliver when available. Publication date 08 Oct 2024

Product Details
  • Dimensions: 138 x 216mm
  • Publication Date: 08 Oct 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: United Kingdom
  • Language: English
  • ISBN13: 9780367706623

About Valentin A. Boicea

AdrianValentin 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.

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