Hybrid Intelligent Technologies in Energy Demand Forecasting

Authors: Hong, Wei-Chiang

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  • Describes the most advanced and accurate energy demand forecasting models
  • Demonstrates how cutting-edge hybrid intelligent technologies can be combined with traditional models
  • Includes a wealth of examples and illustrations to demonstrate the effectiveness of modern demand forecasting models
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eBook $109.00
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  • ISBN 978-3-030-36529-5
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  • Immediate eBook download after purchase
Hardcover $149.99
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  • ISBN 978-3-030-36528-8
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About this book

This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies. 

It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory. 

The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.


About the authors

Wei-Chiang Hong is a professor in the Department of Information Management at the Oriental Institute of Technology, Taiwan. His research interests are focused on hybridized meta-heuristic algorithms (the genetic algorithm, simulated annealing algorithm, immune algorithm, particle swarm optimization algorithm, ant colony / artificial bee colony optimization algorithm, cuckoo search algorithm, bat algorithm, dragonfly algorithm, etc.) together with the chaotic mapping mechanism, quantum computing mechanism, recurrent neural networks, seasonal mechanism, phase space reconstruction, and recurrence plot theory in the support vector regression (SVR) model, the goal being to provide more accurate forecasting performance by determining the suitable parameters of an SVR model. In this regard, the author has gathered substantial practical experience using hybrid meta-heuristic algorithms with intelligent technologies to improve forecasting accuracy.



Table of contents (6 chapters)

Table of contents (6 chapters)

Buy this book

eBook $109.00
price for Mexico (gross)
  • ISBN 978-3-030-36529-5
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for Mexico
  • ISBN 978-3-030-36528-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Hybrid Intelligent Technologies in Energy Demand Forecasting
Authors
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-36529-5
DOI
10.1007/978-3-030-36529-5
Hardcover ISBN
978-3-030-36528-8
Edition Number
1
Number of Pages
XII, 179
Number of Illustrations
9 b/w illustrations, 51 illustrations in colour
Topics