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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 and 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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Abstract

. 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.

Item URL in elib:https://elib.dlr.de/192469/
Document Type:Conference or Workshop Item (Speech)
Title:How Can Deep Learning Be Used To Improve The Heliostat Field Calibration, Even With Small Data Sets? - A Transfer Learning Comparison Study
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pargmann, MaxUNSPECIFIEDhttps://orcid.org/0000-0002-4705-6285UNSPECIFIED
Maldonado Quinto, DanielUNSPECIFIEDhttps://orcid.org/0000-0003-2929-8667UNSPECIFIED
Date:2021
Journal or Publication Title:26th International Conference on Concentrating Solar Power and Chemical Energy Systems, SolarPACES 2020
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1063/5.0085731
ISSN:0094-243X
ISBN:978-073544195-8
Status:Published
Keywords:Neural Network, Solar Power Plant, Deep Learning, Calibration
Event Title:Solar Paces 2020
Event Location:Albuquerque - Online
Event Type:international Conference
Event Start Date:28 September 2020
Event End Date:2 October 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Transport System
DLR - Research area:Transport
DLR - Program:V VS - Verkehrssystem
DLR - Research theme (Project):V - Energie und Verkehr (old)
Location: Köln-Porz
Institutes and Institutions:Institute of Solar Research > Solar Power Plant Technology
Deposited By: Pargmann, Max
Deposited On:23 Dec 2022 09:53
Last Modified:18 Feb 2025 12:35

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