Energy Management : Big Data in Power Load Forecasting book cover
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Energy Management
Big Data in Power Load Forecasting



  • Available for pre-order. Item will ship after June 23, 2021
ISBN 9780367706166
June 23, 2021 Forthcoming by CRC Press
88 Pages 10 B/W Illustrations

 
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Book Description

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.

Table of Contents

1. Introduction 2. General aspects related to the field of Big data and Big data in Power Engineering 2.1. Background of the Big data analytics 2.2. The Big data and the power system: some general aspects 2.3. The Big data in the power system: some characteristic features about storage and analytics 3. Big data in power load forecast 3.1. Big data in power load forecast at distribution level 3.2. Big data in power load forecast at power transmission level 4.Conclusions and further research directions

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Author(s)

Biography

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.