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Record Nr. |
UNINA9910373947203321 |
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Autore |
Hong Wei-Chiang |
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Titolo |
Hybrid Intelligent Technologies in Energy Demand Forecasting / / by Wei-Chiang Hong |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XII, 179 p. 60 illus., 51 illus. in color.) |
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Disciplina |
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Soggetti |
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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 |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 . |
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Sommario/riassunto |
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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 |
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