| Autor: |
Bonanos, Aristides M., Blanco, Manuel J., Milidonis, Kypros, Richter, Christoph |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2020, Vol. 2303 Issue 1, p1-8, 8p |
| Abstrakt: |
Daily reflectance measurements on mirrors used in CSP applications were obtained over a period of 4 months to monitor soiling as a function of meteorological and environmental parameters. The mirrors were placed at 45° increments from face down to face up, and the parameters monitored included temperature, relative humidity, wind speed, rainfall and particulate matter. In order to determine a relationship between the input parameters and the soiling, both multiple linear regression and artificial neural network models were employed. The feed-forward back-propagation neural network with two layers and 16 neurons per layer is the configuration that reaches the best predictive results without overfitting the data, reaching a correlation coefficient of 0.84, compared to a maximum correlation coefficient of 0.60 with the multiple linear regression allowing for quadratic interaction of terms. The model is used to predict the number of annual cleaning operations required for a CSP plant placed in a location with a similar climactic profile, in order to estimate the cleaning contribution to the annual operation and maintenance cost. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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