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Title: An investigation of the key parameters for predicting PV soiling losses

Journal Article · · Progress in Photovoltaics
DOI: https://doi.org/10.1002/pip.2860 · OSTI ID:1351855
 [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)

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.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Renewable Energy. Solar Energy Technologies Office; USDOE
Grant/Contract Number:
AC36-08GO28308; DE‐AC36‐08GO28308
OSTI ID:
1351855
Alternate ID(s):
OSTI ID: 1401750
Report Number(s):
NREL/JA-5J00-67625
Journal Information:
Progress in Photovoltaics, Vol. 25, Issue 4; ISSN 1062-7995
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 97 works
Citation information provided by
Web of Science

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Cited By (4)

Soiling of Photovoltaic Modules: Comparing between Two Distinct Locations within the Framework of Developing the Photovoltaic Soiling Index (PVSI) journal August 2019
A method to predict solar photovoltaic soiling using artificial neural networks and multiple linear regression models journal July 2019
An investigation of the dust accumulation on photovoltaic panels journal November 2019
Predicting photovoltaic soiling losses using environmental parameters: An update
  • Micheli, Leonardo; Deceglie, Michael G.; Muller, Matthew
  • Progress in Photovoltaics: Research and Applications, Vol. 27, Issue 3 https://doi.org/10.1002/pip.3079
journal October 2018