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How Can Deep Learning Be Used To Improve The Heliostat Field Calibration, Even With Small Data Sets? - A Transfer Learning Comparison Study

Pargmann, Max und Maldonado Quinto, Daniel (2021) How Can Deep Learning Be Used To Improve The Heliostat Field Calibration, Even With Small Data Sets? - A Transfer Learning Comparison Study. In: 26th International Conference on Concentrating Solar Power and Chemical Energy Systems, SolarPACES 2020. Solar Paces 2020, 2020-09-28 - 2020-10-02, Albuquerque - Online. doi: 10.1063/5.0085731. ISBN 978-073544195-8. ISSN 0094-243X.

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Kurzfassung

. A precise and reliable alignment of the two-axis heliostat tracking is of great importance for an efficient operation of solar power towers. In order to minimize the tracking error of heliostats, especially in large plants, it is essential to recalibrate the heliostat control unit regularly. Conventional calibration methods with regression can meet the requirements of frequent and regular use, but they cannot adequately account for the many factors that influence alignment. Deep learning algorithms have made remarkable progress in recent years and have the potential to reduce the number of calibrations over time while reducing tracking errors. However, neural networks are still rarely used for such purposes, because such algorithms usually require an extremely large amount of data to map the individual heliostat errors. We present a comparison of different pre-train studies for neural networks to reduce the amount of data per heliostat which are needed to improve the accuracy compared to a state-of-the-art method by applying supervised as well as unsupervised pretraining.

elib-URL des Eintrags:https://elib.dlr.de/192469/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:How Can Deep Learning Be Used To Improve The Heliostat Field Calibration, Even With Small Data Sets? - A Transfer Learning Comparison Study
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Pargmann, MaxMax.Pargmann (at) dlr.dehttps://orcid.org/0000-0002-4705-6285NICHT SPEZIFIZIERT
Maldonado Quinto, DanielDaniel.MaldonadoQuinto (at) dlr.dehttps://orcid.org/0000-0003-2929-8667NICHT SPEZIFIZIERT
Datum:2021
Erschienen in:26th International Conference on Concentrating Solar Power and Chemical Energy Systems, SolarPACES 2020
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
DOI:10.1063/5.0085731
ISSN:0094-243X
ISBN:978-073544195-8
Status:veröffentlicht
Stichwörter:Neural Network, Solar Power Plant, Deep Learning, Calibration
Veranstaltungstitel:Solar Paces 2020
Veranstaltungsort:Albuquerque - Online
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:28 September 2020
Veranstaltungsende:2 Oktober 2020
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Verkehrssystem
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V VS - Verkehrssystem
DLR - Teilgebiet (Projekt, Vorhaben):V - Energie und Verkehr (alt)
Standort: Köln-Porz
Institute & Einrichtungen:Institut für Solarforschung > Solare Kraftwerktechnik
Hinterlegt von: Pargmann, Max
Hinterlegt am:23 Dez 2022 09:53
Letzte Änderung:24 Apr 2024 20:53

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