LEADER 04248nam  22006495  450 
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010   $a3-319-69889-3
024 7 $a10.1007/978-3-319-69889-2
035   $a(CKB)4100000008701641
035   $a(MiAaPQ)EBC5825089
035   $a(DE-He213)978-3-319-69889-2
035   $a(PPN)238489507
035   $a(EXLCZ)994100000008701641
100   $a20190712d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aAdvances on Computational Intelligence in Energy $eThe Applications of Nature-Inspired Metaheuristic Algorithms in Energy /$fedited by Tutut Herawan, Haruna Chiroma, Jemal H. Abawajy
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (228 pages)
225 1 $aGreen Energy and Technology,$x1865-3537
311 08$a3-319-69888-5 
327   $aBasic 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. .
330   $aAddressing the applications of computational intelligence algorithms in energy, this book presents a systematic procedure that illustrates the practical steps required for applying bio-inspired, meta-heuristic algorithms in energy, such as the prediction of oil consumption and other energy products. Contributions include research findings, projects, surveying work and industrial experiences that describe significant advances in the applications of computational intelligence algorithms in energy. For easy understanding, the text provides practical simulation results, convergence and learning curves as well as illustrations and tables. Providing a valuable resource for undergraduate and postgraduate students alike, it is also intended for researchers in the fields of computational intelligence and energy.
410  0$aGreen Energy and Technology,$x1865-3537
606   $aElectric power production
606   $aComputational intelligence
606   $aAlgorithms
606   $aEnergy policy
606   $aEnergy and state
606   $aElectrical Power Engineering
606   $aMechanical Power Engineering
606   $aComputational Intelligence
606   $aAlgorithms
606   $aEnergy Policy, Economics and Management
615  0$aElectric power production.
615  0$aComputational intelligence.
615  0$aAlgorithms.
615  0$aEnergy policy.
615  0$aEnergy and state.
615 14$aElectrical Power Engineering.
615 24$aMechanical Power Engineering.
615 24$aComputational Intelligence.
615 24$aAlgorithms.
615 24$aEnergy Policy, Economics and Management.
676   $a006.3
702   $aHerawan$b Tutut$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aChiroma$b Haruna$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aAbawajy$b Jemal H$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910337596503321
996   $aAdvances on Computational Intelligence in Energy$91930039
997   $aUNINA