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A purely data-driven deep learning digital twin approach for a heliostat field for flux density predictions

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, 2022-09-12 - 2022-09-14, Zürich, Schweiz.

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Item URL in elib:https://elib.dlr.de/193388/
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:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kuhl, MathiasUNSPECIFIEDhttps://orcid.org/0000-0003-0097-7260UNSPECIFIED
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 Start Date:12 September 2022
Event End Date:14 September 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:24 Apr 2024 20:54

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