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Kuhl, Mathias (2022) A purely data-driven deep learning digital twin approach for a heliostat field for flux density predictions. SFERA III / 16th SOLLAB Doctoral Colloquium, 12.-14. Sep 2022, Zürich, Schweiz.
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| Item URL in elib: | https://elib.dlr.de/193388/ | ||||||||
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| Document Type: | Conference or Workshop Item (Speech) | ||||||||
| Title: | A purely data-driven deep learning digital twin approach for a heliostat field for flux density predictions | ||||||||
| Authors: |
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| Date: | 12 September 2022 | ||||||||
| Refereed publication: | No | ||||||||
| Open Access: | No | ||||||||
| Gold Open Access: | No | ||||||||
| In SCOPUS: | No | ||||||||
| In ISI Web of Science: | No | ||||||||
| Status: | Published | ||||||||
| Keywords: | CSP, Machine Learning, Digital Twin, Artificial Intelligence, Flux Density Prediction | ||||||||
| Event Title: | SFERA III / 16th SOLLAB Doctoral Colloquium | ||||||||
| Event Location: | Zürich, Schweiz | ||||||||
| Event Type: | international Conference | ||||||||
| Event Dates: | 12.-14. Sep 2022 | ||||||||
| Organizer: | ETH Zürich | ||||||||
| HGF - Research field: | Energy | ||||||||
| HGF - Program: | Energy System Design | ||||||||
| HGF - Program Themes: | Digitalization and System Technology | ||||||||
| DLR - Research area: | Energy | ||||||||
| DLR - Program: | E SY - Energy System Technology and Analysis | ||||||||
| DLR - Research theme (Project): | E - Energy System Technology, R - Artificial Intelligence | ||||||||
| Location: | Köln-Porz | ||||||||
| Institutes and Institutions: | Institute of Solar Research > Solar Power Plant Technology | ||||||||
| Deposited By: | Kuhl, Mathias | ||||||||
| Deposited On: | 25 Jan 2023 13:38 | ||||||||
| Last Modified: | 25 Jan 2023 13:38 |
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