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. Solar Paces 2020, 28. Sept. - 02. Okt. 2020, Albuquerque - Online. (eingereichter Beitrag)
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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/137618/ | ||||||||||||
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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: |
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Datum: | 2021 | ||||||||||||
Referierte Publikation: | Ja | ||||||||||||
Open Access: | Nein | ||||||||||||
Gold Open Access: | Nein | ||||||||||||
In SCOPUS: | Nein | ||||||||||||
In ISI Web of Science: | Nein | ||||||||||||
Status: | eingereichter Beitrag | ||||||||||||
Stichwörter: | Neural Network, Solar Power Plant, Deep Learning, Calibration | ||||||||||||
Veranstaltungstitel: | Solar Paces 2020 | ||||||||||||
Veranstaltungsort: | Albuquerque - Online | ||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||
Veranstaltungsdatum: | 28. Sept. - 02. Okt. 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: | 16 Nov 2020 16:38 | ||||||||||||
Letzte Änderung: | 16 Nov 2020 16:38 |
Verfügbare Versionen dieses Eintrags
- How Can Deep Learning Be Used To Improve The Heliostat Field Calibration, Even With Small Data Sets? - A Transfer Learning Comparison Study. (deposited 16 Nov 2020 16:38) [Gegenwärtig angezeigt]
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