Hybrid Intelligent Technologies in Energy Demand Forecasting

✍ Scribed by Wei-Chiang Hong

Publisher: Springer International Publishing
Year: 2020
Tongue: English
Leaves: 188
Edition: 1st ed. 2020
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xii
Introduction (Wei-Chiang Hong)....Pages 1-24
Modeling for Energy Demand Forecasting (Wei-Chiang Hong)....Pages 25-44
Data Pre-processing Methods (Wei-Chiang Hong)....Pages 45-67
Hybridizing Meta-heuristic Algorithms with CMM and QCM for SVR’s Parameters Determination (Wei-Chiang Hong)....Pages 69-133
Hybridizing QCM with Dragonfly Algorithm to Enrich the Solution Searching Behaviors (Wei-Chiang Hong)....Pages 135-152
Phase Space Reconstruction and Recurrence Plot Theory (Wei-Chiang Hong)....Pages 153-179

✦ Subjects

Energy; Energy Policy, Economics and Management; Computational Intelligence; Applications of Nonlinear Dynamics and Chaos Theory; Renewable and Green Energy

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