東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Hybrid intelligent technologies in e...

Hong, Wei-Chiang.

FindBook

Google Book

Amazon

博客來

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 forecasting/ by 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.
內容註:	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
標題:	Energy consumption - Forecasting -
電子資源:	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
LDR:01970nmm a2200337 a 4500 001 2215107
003 DE-He213
005 20200514172449.0
006 m d
007 cr nn 008maaau
008 201119s2020 sz s 0 eng d
020 $a 9783030365295 $q (electronic bk.)
020 $a 9783030365288 $q (paper)
024 7 $a 10.1007/978-3-030-36529-5 $2 doi
035 $a 978-3-030-36529-5
040 $a GP $c GP
041 0 $a eng
050 4 $a HD9502.A2 $b H664 2020
072 7 $a TH $2 bicssc
072 7 $a BUS070040 $2 bisacsh
072 7 $a TH $2 thema
072 7 $a KNB $2 thema
082 0 4 $a 333.79 $2 23
090 $a HD9502.A2 $b H772 2020
100 1 $a Hong, Wei-Chiang. $3 927794
245 1 0 $a Hybrid intelligent technologies in energy demand forecasting $h [electronic resource] / $c by Wei-Chiang Hong.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
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
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u https://doi.org/10.1007/978-3-030-36529-5
950 $a Energy (Springer-40367)