One hundred and two environmental and meteorological parameters have been investigated and compared with the performance of 20 soiling stations installed in the USA, in order to determine their ability to predict the soiling losses occurring on PV systems. The results of this investigation showed that the annual average of the daily mean particulate matter values recorded by monitoring stations deployed near the PV systems are the best soiling predictors, with coefficients of determination (R2) as high as 0.82. The precipitation pattern was also found to be relevant: among the different meteorological parameters, the average length of dry periods had the best correlation with the soiling ratio. A preliminary investigation of two-variable regressions was attempted and resulted in an adjusted R2 of 0.90 when a combination of PM2.5 and a binary classification for the average length of the dry period was introduced. Copyright © 2017 John Wiley & Sons, Ltd.

An investigation of the key parameters for predicting PV soiling losses / Micheli, L.; Muller, M.. - In: PROGRESS IN PHOTOVOLTAICS. - ISSN 1062-7995. - 25:4(2017), pp. 291-307. [10.1002/pip.2860]

An investigation of the key parameters for predicting PV soiling losses

Micheli L.
;
2017

Abstract

One hundred and two environmental and meteorological parameters have been investigated and compared with the performance of 20 soiling stations installed in the USA, in order to determine their ability to predict the soiling losses occurring on PV systems. The results of this investigation showed that the annual average of the daily mean particulate matter values recorded by monitoring stations deployed near the PV systems are the best soiling predictors, with coefficients of determination (R2) as high as 0.82. The precipitation pattern was also found to be relevant: among the different meteorological parameters, the average length of dry periods had the best correlation with the soiling ratio. A preliminary investigation of two-variable regressions was attempted and resulted in an adjusted R2 of 0.90 when a combination of PM2.5 and a binary classification for the average length of the dry period was introduced. Copyright © 2017 John Wiley & Sons, Ltd.
2017
linear regression; particulate matter; photovoltaic performance; precipitation; soiling; soiling losses
01 Pubblicazione su rivista::01a Articolo in rivista
An investigation of the key parameters for predicting PV soiling losses / Micheli, L.; Muller, M.. - In: PROGRESS IN PHOTOVOLTAICS. - ISSN 1062-7995. - 25:4(2017), pp. 291-307. [10.1002/pip.2860]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1625654
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