Intelligent Energy Demand Forecasting

  • Wei-Chiang Hong

Part of the Lecture Notes in Energy book series (LNEN, volume 10)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Wei-Chiang Hong
    Pages 1-20
  3. Wei-Chiang Hong
    Pages 21-40

About this book

Introduction

As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand.

 

Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms.

 

Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools.

Keywords

Chaos theory Energy forecasting Evolutionary algorithms Short term forecasting Support vector regression

Authors and affiliations

  • Wei-Chiang Hong
    • 1
  1. 1., Department of Information ManagementOriental Institute of TechnologyNew Taipei CityTaiwan R.O.C.

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-4968-2
  • Copyright Information Springer-Verlag London 2013
  • Publisher Name Springer, London
  • eBook Packages Energy
  • Print ISBN 978-1-4471-4967-5
  • Online ISBN 978-1-4471-4968-2
  • Series Print ISSN 2195-1284
  • Series Online ISSN 2195-1292
  • About this book
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