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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. Solar Paces 2020, 28. Sept. - 02. Okt. 2020, Albuquerque - Online. (Submitted)

Full text not available from this repository.

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/137618/
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 iD
Pargmann, MaxMax.Pargmann (at) dlr.dehttps://orcid.org/0000-0002-4705-6285
Maldonado Quinto, DanielDaniel.MaldonadoQuinto (at) dlr.deUNSPECIFIED
Date:2021
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Submitted
Keywords:Neural Network, Solar Power Plant, Deep Learning, Calibration
Event Title:Solar Paces 2020
Event Location:Albuquerque - Online
Event Type:international Conference
Event Dates:28. Sept. - 02. Okt. 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
Location: Köln-Porz
Institutes and Institutions:Institute of Solar Research > Solar Power Plant Technology
Deposited By: Pargmann, Max
Deposited On:16 Nov 2020 16:38
Last Modified:16 Nov 2020 16:38

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