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Hybrid intelligent technologies in e...

Hong, Wei-Chiang.

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Hybrid intelligent technologies in energy demand forecasting

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hybrid intelligent technologies in energy demand forecasting/ by Wei-Chiang Hong.
Author:	Hong, Wei-Chiang.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xii, 179 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Energy consumption - Forecasting -
Online resource:	https://doi.org/10.1007/978-3-030-36529-5
ISBN:	9783030365295

Hybrid intelligent technologies in energy demand forecasting
Hong, Wei-Chiang.

Hybrid intelligent technologies in energy demand forecasting[electronic resource] /by Wei-Chiang Hong. - Cham :Springer International Publishing :2020. - xii, 179 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030365295

Standard No.: 10.1007/978-3-030-36529-5doiSubjects--Topical Terms:

3446250
Energy consumption
--Forecasting

LC Class. No.: HD9502.A2 / H664 2020

Dewey Class. No.: 333.79

Hybrid intelligent technologies in energy demand forecasting
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245 1 0 $a Hybrid intelligent technologies in energy demand forecasting $h [electronic resource] / $c by Wei-Chiang Hong.
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300 $a xii, 179 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Energy consumption $x Forecasting $x Data processing. $3 3446250
650 0 $a Artificial intelligence. $3 516317
650 1 4 $a Energy Policy, Economics and Management. $3 1532761
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Simulation and Modeling. $3 890873
650 2 4 $a Applications of Nonlinear Dynamics and Chaos Theory. $3 3134772
650 2 4 $a Renewable and Green Energy. $3 928108
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950 $a Energy (Springer-40367)