Description

Book Synopsis

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. 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.

Energy Management

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    A Hardback by Valentin A. Boicea

    £62.58

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    Order before 4pm on Monday for delivery by Fri 4 Dec 2026.

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      Book details

      Publisher Taylor & Francis
      Published 28 June 2021
      ISBN-13 9780367706166
      978-0367706166
      ISBN-10 0367706164
      Also in:
      Mathematical modelling Energy

      Description

      Book Synopsis

      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. 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.

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