Hybrid Intelligent Technologies In Energy Demand Forecasting - Hong Wei-Chiang | Libro Springer 01/2020 - HOEPLI.it


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hong wei-chiang - hybrid intelligent technologies in energy demand forecasting

Hybrid Intelligent Technologies in Energy Demand Forecasting




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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 01/2020
Edizione: 1st ed. 2020





Sommario

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 





Trama

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.






Autore

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.










Altre Informazioni

ISBN:

9783030365288

Condizione: Nuovo
Dimensioni: 235 x 155 mm Ø 459 gr
Formato: Copertina rigida
Illustration Notes:9 Illustrations, black and white
Pagine Arabe: 179
Pagine Romane: xii






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