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

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. Lastly, 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.

Authors:
 [1];  [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (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. https://doi.org/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 = {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. Lastly, 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.},
doi = {10.1002/pip.2860},
journal = {Progress in Photovoltaics},
number = 4,
volume = 25,
place = {United States},
year = {2017},
month = {1}
}

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Cited by: 4 works
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    Works referencing / citing this record:

    World Map of the Köppen-Geiger climate classification updated [World Map of the Köppen-Geiger climate classification updated]
    journal, July 2006


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    • Feliu, S.; Morcillo, M.; Chico, B.
    • CORROSION, Vol. 55, Issue 9
    • DOI: 10.5006/1.3284045

    Modelling sea-salt transport and deposition in marine atmosphere zone – A tool for corrosion studies
    journal, September 2008


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    journal, March 2016

    • van Donkelaar, Aaron; Martin, Randall V.; Brauer, Michael
    • Environmental Science & Technology, Vol. 50, Issue 7
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    Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data
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    • Cole, I. S.; Paterson, D. A.; Ganther, W. D.
    • Corrosion Engineering, Science and Technology, Vol. 38, Issue 2
    • DOI: 10.1179/147842203767789203

    Assessment of PM dry deposition on solar energy harvesting systems: Measurement–model comparison
    journal, February 2016


    Measurements and modelling of marine salt transportation and deposition in a tropical region in Brazil
    journal, September 2006


    Systematic experimental study of the pollution deposition impact on the energy yield of photovoltaic installations
    journal, October 2011


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    journal, December 2010


    Soiling losses for solar photovoltaic systems in California
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    Predicting photovoltaic soiling losses using environmental parameters: An update
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    A method to predict solar photovoltaic soiling using artificial neural networks and multiple linear regression models
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