LEADER 04439nam 22006255 450 001 9910337596503321 005 20200705130517.0 010 $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$b[electronic resource] $eThe Applications of Nature-Inspired Metaheuristic Algorithms in Energy /$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 $aGreen Energy and Technology,$x1865-3529 311 $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 of Australia 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,$x1865-3529 606 $aEnergy systems 606 $aComputational intelligence 606 $aAlgorithms 606 $aEnergy policy 606 $aEnergy and state 606 $aEnergy Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/115000 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 606 $aEnergy Policy, Economics and Management$3https://scigraph.springernature.com/ontologies/product-market-codes/112000 615 0$aEnergy systems. 615 0$aComputational intelligence. 615 0$aAlgorithms. 615 0$aEnergy policy. 615 0$aEnergy and state. 615 14$aEnergy Systems. 615 24$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