An investigation of the key parameters for predicting PV soiling losses
Abstract
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 ( R 2 ) 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 R 2 of 0.90 when a combination of PM 2.5 and a binary classification for the average length of the dry period was introduced. Copyright © 2017 John Wiley & Sons, Ltd.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Publication Date:
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
- OSTI Identifier:
- 1351855
- Alternate Identifier(s):
- OSTI ID: 1401750
- Report Number(s):
- NREL/JA-5J00-67625
Journal ID: ISSN 1062-7995
- Grant/Contract Number:
- AC36-08GO28308; DE‐AC36‐08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Progress in Photovoltaics
- Additional Journal Information:
- Journal Volume: 25; Journal Issue: 4; Journal ID: ISSN 1062-7995
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; soiling; photovoltaic performance; soiling losses; particulate matter; precipitation; linear regression
Citation Formats
Micheli, Leonardo, and Muller, Matthew. An investigation of the key parameters for predicting PV soiling losses. United States: N. p., 2017.
Web. doi:10.1002/pip.2860.
Micheli, Leonardo, & Muller, Matthew. An investigation of the key parameters for predicting PV soiling losses. United States. https://doi.org/10.1002/pip.2860
Micheli, Leonardo, and Muller, Matthew. Wed .
"An investigation of the key parameters for predicting PV soiling losses". United States. https://doi.org/10.1002/pip.2860. https://www.osti.gov/servlets/purl/1351855.
@article{osti_1351855,
title = {An investigation of the key parameters for predicting PV soiling losses},
author = {Micheli, Leonardo and Muller, Matthew},
abstractNote = {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 ( R 2 ) 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 R 2 of 0.90 when a combination of PM 2.5 and a binary classification for the average length of the dry period was introduced. Copyright © 2017 John Wiley & Sons, Ltd.},
doi = {10.1002/pip.2860},
journal = {Progress in Photovoltaics},
number = 4,
volume = 25,
place = {United States},
year = {Wed Jan 25 00:00:00 EST 2017},
month = {Wed Jan 25 00:00:00 EST 2017}
}
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Works referencing / citing this record:
Soiling of Photovoltaic Modules: Comparing between Two Distinct Locations within the Framework of Developing the Photovoltaic Soiling Index (PVSI)
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