Hybrid Intelligent Technologies in Energy Demand Forecasting

1. Auflage 2020
ISBN: 978-3-030-36531-8
Verlag: Springer International Publishing

Buch, Englisch, 179 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 300 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Hong, Wei-Chiang

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Alternative und erneuerbare Energien
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Technische Wissenschaften Technik Allgemein Modellierung & Simulation
• Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Energie- & Versorgungswirtschaft Energiewirtschaft: Alternative & Erneuerbare Energien
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computersimulation & Modelle, 3-D Graphik

Inhaltsverzeichnis

Introduction.- Modeling for Energy Demand Forecasting.-  Data Pre-processing Methods.- Hybridizing Meta-heuristic Algorithms with CMM and QCM for SVR’s Parameters Determination.- Hybridizing QCM with Dragonfly algorithm to Enrich the Solution Searching Be-haviors.- Phase Space Reconstruction and Recurrence Plot Theory 

Über Autor(innen)

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.