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978-3-319-69889-2 |
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DE-He213 |
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20240322020201.0 |
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190712s2019 sz | s |||| 0|eng d |
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|a 9783319698892
|9 978-3-319-69889-2
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|a 10.1007/978-3-319-69889-2
|2 doi
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|a TK1001-1841
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|a THR
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|a TEC031020
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|a 621.31
|2 23
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|a Advances on Computational Intelligence in Energy
|h [electronic resource] :
|b The Applications of Nature-Inspired Metaheuristic Algorithms in Energy /
|c edited by Tutut Herawan, Haruna Chiroma, Jemal H. Abawajy.
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| 250 |
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|a 1st ed. 2019.
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| 264 |
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1 |
|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2019.
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| 300 |
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|a XIV, 215 p.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a Green Energy and Technology,
|x 1865-3537
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| 505 |
0 |
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|a Basic descriptions of computational intelligence algorithms (single, hybrid, ensemble, integrated and etc -- Credible sources of energy datasets -- Applications of computational algorithms in energy -- Practical application of cuckoo search and neural network in the prediction of OECD oil consumption -- Hybrid of Fuzzy systems and particle swarm optimization in the forecasting gas flaring from oil consumption -- Forecasting of OECD gas flaring using Elman neural network and cuckoo search algorithm -- Artificial bee colony and neural network for the forecasting of Malaysia renewable energy -- Soft computing methods in the modelling of OECD carbon dioxide emission from petroleum consumption -- Modelling energy crises based on Soft computing -- The forecasting of WTI and Dubai crude oil prices benchmarks based on soft computing -- A new approach for the forecasting of IAEA energy -- Modelling of gasoline prices using fuzzy multi-criteria decision making -- Soft computing for the prediction ofAustralia petroleum consumption based on OECD countries -- Future research problems in the area of computational intelligence algorithms in energy. .
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| 520 |
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|a Addressing the applications of computational intelligence algorithms in energy, this book presents a systematic procedure that illustrates the practical steps required for applying bio-inspired, meta-heuristic algorithms in energy, such as the prediction of oil consumption and other energy products. Contributions include research findings, projects, surveying work and industrial experiences that describe significant advances in the applications of computational intelligence algorithms in energy. For easy understanding, the text provides practical simulation results, convergence and learning curves as well as illustrations and tables. Providing a valuable resource for undergraduate and postgraduate students alike, it is also intended for researchers in the fields of computational intelligence and energy.
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| 650 |
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0 |
|a Electric power production.
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| 650 |
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0 |
|a Computational intelligence.
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| 650 |
|
0 |
|a Algorithms.
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| 650 |
|
0 |
|a Energy policy.
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| 650 |
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0 |
|a Energy and state.
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| 650 |
1 |
4 |
|a Electrical Power Engineering.
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| 650 |
2 |
4 |
|a Mechanical Power Engineering.
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| 650 |
2 |
4 |
|a Computational Intelligence.
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| 650 |
2 |
4 |
|a Algorithms.
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| 650 |
2 |
4 |
|a Energy Policy, Economics and Management.
|
| 700 |
1 |
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|a Herawan, Tutut.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Chiroma, Haruna.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Abawajy, Jemal H.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
2 |
|
|a SpringerLink (Online service)
|
| 773 |
0 |
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|t Springer Nature eBook
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783319698885
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| 776 |
0 |
8 |
|i Printed edition:
|z 9783319698908
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| 830 |
|
0 |
|a Green Energy and Technology,
|x 1865-3537
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-69889-2
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| 912 |
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|a ZDB-2-ENE
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| 912 |
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|a ZDB-2-SXEN
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| 950 |
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|a Energy (SpringerNature-40367)
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| 950 |
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|a Energy (R0) (SpringerNature-43717)
|