Automated Shmoo data analysis: A machine learning approach
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: 2014DOI: 10.1109/PESGM.2014.6939004
Keyword(s): air pollution control,hydrogen storage,natural gas technology,renewable energy sources,European energy system,energy surplus,greenhouse gas emission reduction,long-term energy storage,natural gas infrastructure,power-to-gas technology,renewable energy sources,Electricity,Europe,Hydrogen,Load modeling,Natural gas,Power generation,Production,Energy storage,Hydrogen storage,Power generation dispatch,Power-to-Gas
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Automated Shmoo data analysis: A machine learning approach
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| contributor author | Wei Wang | |
| date accessioned | 2020-03-12T19:37:32Z | |
| date available | 2020-03-12T19:37:32Z | |
| date issued | 2014 | |
| identifier other | 6783327.pdf | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/984028?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Automated Shmoo data analysis: A machine learning approach | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8099379 | |
| subject keywords | air pollution control | |
| subject keywords | hydrogen storage | |
| subject keywords | natural gas technology | |
| subject keywords | renewable energy sources | |
| subject keywords | European energy system | |
| subject keywords | energy surplus | |
| subject keywords | greenhouse gas emission reduction | |
| subject keywords | long-term energy storage | |
| subject keywords | natural gas infrastructure | |
| subject keywords | power-to-gas technology | |
| subject keywords | renewable energy sources | |
| subject keywords | Electricity | |
| subject keywords | Europe | |
| subject keywords | Hydrogen | |
| subject keywords | Load modeling | |
| subject keywords | Natural gas | |
| subject keywords | Power generation | |
| subject keywords | Production | |
| subject keywords | Energy storage | |
| subject keywords | Hydrogen storage | |
| subject keywords | Power generation dispatch | |
| subject keywords | Power-to-Gas | |
| identifier doi | 10.1109/PESGM.2014.6939004 | |
| journal title | uality Electronic Design (ISQED), 2014 15th International Symposium on | |
| filesize | 733851 | |
| citations | 0 |