Advances on Computational Intelligence in Energy The Applications of Nature-Inspired Metaheuristic Algorithms in Energy /
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....
| Συγγραφή απο Οργανισμό/Αρχή: | |
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| Άλλοι συγγραφείς: | , , |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2019.
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| Έκδοση: | 1st ed. 2019. |
| Σειρά: | Green Energy and Technology,
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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 of Australia petroleum consumption based on OECD countries
- Future research problems in the area of computational intelligence algorithms in energy. .
