Hybrid Intelligent Technologies in Energy Demand Forecasting
Titre:
Hybrid Intelligent Technologies in Energy Demand Forecasting
ISBN (Numéro international normalisé des livres):
9783030365295
Auteur personnel:
Edition:
1st ed. 2020.
PRODUCTION_INFO:
Cham : Springer International Publishing : Imprint: Springer, 2020.
Description physique:
XII, 179 p. 60 illus., 51 illus. in color. online resource.
Table des matières:
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 .
Extrait:
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.
Auteur collectif ajouté:
Accès électronique:
Full Text Available From Springer Nature Energy 2020 Packages
Langue:
Anglais