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| 003 | EG-GiCUC | ||
| 005 | 20250223032203.0 | ||
| 008 | 190211s2018 ua dh f m 000 0 eng d | ||
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_aEG-GiCUC _beng _cEG-GiCUC |
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| 041 | 0 | _aeng | |
| 049 | _aDeposite | ||
| 097 | _aM.Sc | ||
| 099 | _aCai01.20.03.M.Sc.2018.Sa.K | ||
| 100 | 0 | _aSarah Osama Talaat Ibrahim | |
| 245 | 1 | 0 |
_aKernel-based swarm optimization for renewable energy application / _cSarah Osama Talaat Ibrahim ; Supervised Aly Aly Fahmy , Aboul Ella Hassanien |
| 246 | 1 | 5 | _aأمثلية الذكاء السربي المعتمد على دالة النواة لتطبيقات الطاقة المتجددة |
| 260 |
_aCairo : _bSarah Osama Talaat Ibrahim , _c2018 |
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| 300 |
_a102 Leaves : _bcharts , facsimiles ; _c30cm |
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| 502 | _aThesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science | ||
| 520 | _aForecasting wind and solar behaviors (e.g., wind speed and global solar radiation) is importantfor energy managers and electricity traders. Moreover, the scientific prediction methods for renewable energy can improve the reliability and efficiency of the renewable power generation units. In the last few years, Support Vector Regression (SVR) has been applied to forecast the renewable energy. The performance and stability of SVR depend on their meta-parameters | ||
| 530 | _aIssued also as CD | ||
| 653 | 4 | _aRenewable Energy | |
| 653 | 4 | _aSolar radiation | |
| 653 | 4 | _aWind speed | |
| 700 | 0 |
_aAboulella Hassanien , _eSupervisor |
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| 700 | 0 |
_aAly Aly Fahmy , _eSupervisor |
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| 856 | _uhttp://172.23.153.220/th.pdf | ||
| 905 |
_aNazla _eRevisor |
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| 905 |
_aShimaa _eCataloger |
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_2ddc _cTH |
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_c70073 _d70073 |
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