High Accuracy Data-Driven Heliostat Calibration and State Prediction with Pretrained Deep Neural Networks

Pargmann, Max and Maldonado Quinto, Daniel and Schwarzbözl, Peter and Pitz-Paal, Robert (2021) High Accuracy Data-Driven Heliostat Calibration and State Prediction with Pretrained Deep Neural Networks. Solar Energy, 218, pp. 48-56. Elsevier. doi: 10.1016/j.solener.2021.01.046. ISSN 0038-092X.

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Item URL in elib:

https://elib.dlr.de/147259/

Document Type:

Article

Title:

High Accuracy Data-Driven Heliostat Calibration and State Prediction with Pretrained Deep Neural Networks

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Pargmann, Max	UNSPECIFIED	https://orcid.org/0000-0002-4705-6285	UNSPECIFIED
Maldonado Quinto, Daniel	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Schwarzbözl, Peter	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Pitz-Paal, Robert	UNSPECIFIED	https://orcid.org/0000-0002-3542-3391	UNSPECIFIED

Date:

April 2021

Journal or Publication Title:

Solar Energy

Refereed publication:

Yes

Open Access:

Yes

Gold Open Access:

In SCOPUS:

Yes

In ISI Web of Science:

Yes

Volume:

218

DOI:

10.1016/j.solener.2021.01.046

Page Range:

pp. 48-56

Publisher:

Elsevier

ISSN:

0038-092X

Status:

Published

Keywords:

Deep Learning, Solar Tower Power Plant, Neural Network, Calibration, Heliostat

HGF - Research field:

Energy

HGF - Program:

Technology, Innovation and Society

HGF - Program Themes:

Renewable Energy and Material Resources for Sustainable Futures - Integrating at Different Scales

DLR - Research area:

Energy

DLR - Program:

E SY - Energy Systems Analysis

DLR - Research theme (Project):

E - Energy Systems Technology (old)

Location:

Köln-Porz

Institutes and Institutions:

Institute of Solar Research > Solar Power Plant Technology

Deposited By:

Pargmann, Max

Deposited On:

13 Dec 2021 14:40

Last Modified:

24 May 2022 23:48

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