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

• Autore: Hong Wei-Chiang
• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
• Edizione: 1st ed. 2020.
• Descrizione fisica: 1 online resource (XII, 179 p. 60 illus., 51 illus. in color.)
• Disciplina: 006.3
• Soggetto topico: 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
• 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.
• Titolo autorizzato: Hybrid Intelligent Technologies in Energy Demand Forecasting
• ISBN: 3-030-36529-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910373947203321
• Lo trovi qui: Univ. Federico II