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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. Elsevier. ISSN 0038-092X.

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Item URL in elib:https://elib.dlr.de/138067/
Document Type:Article
Title:High Accuracy Data-Driven Heliostat Calibration and State Prediction with Pretrained Deep Neural Networks
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Pargmann, MaxMax.Pargmann (at) dlr.dehttps://orcid.org/0000-0002-4705-6285
Maldonado Quinto, DanielDaniel.MaldonadoQuinto (at) dlr.deUNSPECIFIED
Schwarzbözl, PeterPeter.Schwarzboezl (at) dlr.deUNSPECIFIED
Pitz-Paal, RobertRobert.Pitz-Paal (at) dlr.dehttps://orcid.org/0000-0002-3542-3391
Date:2021
Journal or Publication Title:Solar Energy
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Publisher:Elsevier
ISSN:0038-092X
Status:Accepted
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:23 Dec 2020 13:12
Last Modified:23 Dec 2020 13:12

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