1.

Record Nr.

UNINA9910373947203321

Autore

Hong Wei-Chiang

Titolo

Hybrid Intelligent Technologies in Energy Demand Forecasting / / by Wei-Chiang Hong

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020

ISBN

3-030-36529-8

Edizione

[1st ed. 2020.]

Descrizione fisica

1 online resource (XII, 179 p. 60 illus., 51 illus. in color.)

Disciplina

006.3

Soggetti

Energy policy

Energy and state

Computational intelligence

Computer simulation

Statistical physics

Renewable energy resources

Energy Policy, Economics and Management

Computational Intelligence

Simulation and Modeling

Applications of Nonlinear Dynamics and Chaos Theory

Renewable and Green Energy

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di contenuto

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 .

Sommario/riassunto

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.