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

Brenner, Alex; Kahn, James ORCID iD icon 1; Hirsch, Tobias; Röger, Marc; Pitz-Paal, Robert
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)

Abstract:

Advanced cleaning strategies for parabolic trough collectors at concentrated solar power plants maximize the yield and minimize the costs for cleaning activities. However, they require information about the current soiling level of each collector. In this work, a novel, data-driven method for soiling estimation with machine learning for parabolic trough collectors is developed using gloss values as a surrogate for soiling values. Operational data and meteorological data from the solar field Andasol-3 with changing time horizons are used together with various Machine Learning techniques to estimate the soiling of every collector in the field. The best results were achieved with a Decision Tree model, with a coefficient of determination of 𝑅$^2$ = 0.77 from the maximum value of 1 and a mean squared error of 𝑀𝑆𝐸 = 6.14 for the determination of specific soiling values. A second metric to evaluate the quality of soiling predictions from the models classifies whether soiling is above or below a cleaning threshold was also investigated. Model results are compared to soiling measurements that indicate the need for cleanings. Cleaning recommendations are derived and compared with the current fixed-time cleaning schedule of Andasol-3. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000159386
Veröffentlicht am 29.06.2023
Originalveröffentlichung
DOI: 10.1016/j.solener.2023.05.008
Scopus
Zitationen: 1
Web of Science
Zitationen: 1
Dimensions
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 15.07.2023
Sprache Englisch
Identifikator ISSN: 0038-092X, 0038-092x, 1471-1257
KITopen-ID: 1000159386
HGF-Programm 46.21.04 (POF IV, LK 01) HAICU
Erschienen in Solar Energy
Verlag Elsevier
Band 259
Seiten 257–276
Vorab online veröffentlicht am 24.05.2023
Schlagwörter Parabolic trough, Soiling, Machine learning, Artificial neural network, Concentrated solar power
Nachgewiesen in Dimensions
Scopus
Web of Science
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