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Energy Management: Big Data in Power Load Forecasting: CRC Press Focus Shortform Book Program

Autor Valentin A. Boicea
en Limba Engleză Paperback – 8 oct 2024
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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Specificații

ISBN-13: 9780367706623
ISBN-10: 0367706628
Pagini: 92
Ilustrații: 20
Dimensiuni: 138 x 216 mm
Greutate: 0.17 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Seria CRC Press Focus Shortform Book Program

Locul publicării:Boca Raton, United States

Public țintă

Academic and Professional

Cuprins

1. Big Data Analysis Tools: Data Collection and Sampling. 2. Big Data and the Energy Field. 3. The Load Forecast: A New Application for Big Data. 4. Conclusions.

Notă biografică

Adrian–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.

Descriere

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 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.