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Soiling determination for parabolic trough collectors based on operational data analysis and machine learning

Brenner, Alex and Kahn, James and Hirsch, Tobias and Röger, Marc and Pitz-Paal, Robert (2023) Soiling determination for parabolic trough collectors based on operational data analysis and machine learning. Solar Energy. Elsevier. doi: 10.1016/j.solener.2023.05.008. ISSN 0038-092X.

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Item URL in elib:https://elib.dlr.de/195841/
Document Type:Article
Title:Soiling determination for parabolic trough collectors based on operational data analysis and machine learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Brenner, AlexUNSPECIFIEDhttps://orcid.org/0000-0003-0754-0272UNSPECIFIED
Kahn, JamesUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Hirsch, TobiasUNSPECIFIEDhttps://orcid.org/0000-0003-0063-0128UNSPECIFIED
Röger, MarcUNSPECIFIEDhttps://orcid.org/0000-0003-0618-4253UNSPECIFIED
Pitz-Paal, RobertUNSPECIFIEDhttps://orcid.org/0000-0002-3542-3391UNSPECIFIED
Date:24 May 2023
Journal or Publication Title:Solar Energy
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1016/j.solener.2023.05.008
Publisher:Elsevier
ISSN:0038-092X
Status:Published
Keywords:parabolic trough; soiling; machine learning; artificial neural network; concentrated solar power
HGF - Research field:Energy
HGF - Program:Materials and Technologies for the Energy Transition
HGF - Program Themes:High-Temperature Thermal Technologies
DLR - Research area:Energy
DLR - Program:E SW - Solar and Wind Energy
DLR - Research theme (Project):E - Advanced Heat Transfer Media
Location: Stuttgart
Institutes and Institutions:Institute of Solar Research > Solar High Temperature Technologies
Deposited By: Lucarelli, Fabio
Deposited On:18 Oct 2023 11:39
Last Modified:23 Oct 2023 13:05

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