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Hybrid intelligent technologies in e...

Hong, Wei-Chiang.

Hybrid intelligent technologies in energy demand forecasting

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hybrid intelligent technologies in energy demand forecastingby Wei-Chiang Hong.
作者:	Hong, Wei-Chiang.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xii, 179 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Energy consumptionForecasting
電子資源:	https://doi.org/10.1007/978-3-030-36529-5
ISBN:	9783030365295$q(electronic bk.)

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$q(electronic bk.)

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

863744
Energy consumption
--Forecasting

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

Dewey Class. No.: 333.79

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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.
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