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Hybrid Intelligent Technologies in Energy Demand Forecasting

by Wei-Chiang Hong
Save 35% Save 35%
Current price ₹7,345.00
Original price ₹11,299.00
Original price ₹11,299.00
Original price ₹11,299.00
(-35%)
₹7,345.00
Current price ₹7,345.00

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Book cover type: Hardcover
  • ISBN13: 9783030365288
  • Binding: Hardcover
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 179
  • Original Price: EUR 99.99
  • Language: English
  • Edition: 2020
  • Item Weight: 450 grams
  • BISAC Subject(s): Industries / Energy, Engineering (General), and Computer Simulation

From the Back Cover

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.


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.

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