Ray-level Deep-learning-based correction for enhanced universal flux density predictions.

Diaz Alonso, Sergio and Raeder, Christian and Hoffschmidt, Bernhard (2025) Ray-level Deep-learning-based correction for enhanced universal flux density predictions. In: SolarPACES 2025. SolarPACES 2025, 2025-09-23 - 2025-09-26, Almería, Spanien.

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Item URL in elib:

https://elib.dlr.de/218162/

Document Type:

Conference or Workshop Item (Poster)

Title:

Ray-level Deep-learning-based correction for enhanced universal flux density predictions.

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Diaz Alonso, Sergio	sergio.diazalonso (at) dlr.de	https://orcid.org/0009-0001-1283-5502	195570694
Raeder, Christian	Christian.Raeder (at) dlr.de	https://orcid.org/0009-0009-4903-6200	195570695
Hoffschmidt, Bernhard	Bernhard.Hoffschmidt (at) dlr.de	UNSPECIFIED	UNSPECIFIED

Date:

26 September 2025

Journal or Publication Title:

SolarPACES 2025

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Published

Keywords:

Concentrating solar power (CSP); Digital twin; Cyber-Physical System (CPS); Flux Density Measurements (FDM); Real-time simulation

Event Title:

SolarPACES 2025

Event Location:

Almería, Spanien

Event Type:

international Conference

Event Start Date:

23 September 2025

Event End Date:

26 September 2025

HGF - Research field:

Energy

HGF - Program:

Materials and Technologies for the Energy Transition

HGF - Program Themes:

High-Temperature Thermal Technologies

DLR - Research area:

Energy

DLR - Program:

E SW - Solar and Wind Energy

DLR - Research theme (Project):

E - Smart Operation

Location:

Jülich

Institutes and Institutions:

Institute of Solar Research > Concentrating Solar Technologies

Deposited By:

Diaz Alonso, Sergio

Deposited On:

30 Oct 2025 09:22

Last Modified:

04 Nov 2025 08:58

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