Deep learning CNN-LSTM-MLP hybrid fusion model for feature optimizations and daily solar radiation prediction
We greatly acknowledge these for the data (i) Queensland Climate Change Centre of Excellence (QCCCE), a part of the Department of Science, Information Technology, Innovation, and the Arts (DSITIA) (ii) the Centre for Environmental Data Analysis (CEDA) as a server for the CMIP5 project’s GCM output c...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos |
| OAI Identifier: | oai:burjcdigital.urjc.es:10115/24515 |
| Acceso en línea: | https://hdl.handle.net/10115/24515 |
| Access Level: | acceso abierto |
| Palabra clave: | Global solar prediction Deep Learning networks Convolutional Neural Networks Slime Mould Algorithm Renewable energy Global climate models |
| Sumario: | We greatly acknowledge these for the data (i) Queensland Climate Change Centre of Excellence (QCCCE), a part of the Department of Science, Information Technology, Innovation, and the Arts (DSITIA) (ii) the Centre for Environmental Data Analysis (CEDA) as a server for the CMIP5 project’s GCM output collection for CSIRO-BOM ACCESS1-0, MOHC Hadley-GEM2-CC and the MRI MRI-CGCM3. Partial support of this study is through the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN) . |
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