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Title: Predicting photovoltaic soiling losses using environmental parameters: An update

Abstract

This study presents an investigation on the correlations between soiling losses and environmental parameters at 41 locations in the United States, with the aim of analyzing the possibility of predicting soiling losses at a site even when soiling data are not available. The results of this work, which considers the largest pool of soiling data points systematically investigated so far, confirm that a single-variable regression based on particulate matter concentration returns the best correlations with soiling, with adjusted coefficients of determination up to 70%, corresponding to RMSE as low as 0.9%. Among the various particulate matter datasets investigated, a gridded Environment Protection Agency dataset is for the first time found to return correlations similar to those obtained by interpolating particulate matter monitoring station data. We discuss in detail the different interpolation techniques used to process the particulate matter concentrations because they can greatly impact the correlations. Specifically, the correlation coefficients between soiling and particulate matter range between 70% and less than 20%, depending on the interpolation methods and monitoring distance. Spatial interpolation methods based on inverse distance weighting are found to return better correlations than a nearest neighbor or a simple average approach, especially when large distances are considered. Similarly,more » the effects of different rain thresholds used to calculate the length of the dry periods are examined. An enhanced two-variable regression is found to achieve higher-quality correlations, with adjusted R2 of 90% (RMSE = 0.55%), also suggesting that high and low soiling locations might be differentiated depending on fixed particulate matter or rainfall thresholds.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]
  1. National Renewable Energy Laboratory, Golden CO 80401 USA
  2. National Renewable Energy Laboratory, Golden CO 80401 USA; Current: Leidos, Denver CO 80202 USA
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1480682
Report Number(s):
NREL/JA-5K00-71637
Journal ID: ISSN 1062-7995
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Progress in Photovoltaics
Additional Journal Information:
Journal Volume: 2018; Journal Issue: none; Journal ID: ISSN 1062-7995
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 42 ENGINEERING; soiling; photovoltaic performance; soiling losses; particulate matter; precipitation; linear regression

Citation Formats

Micheli, Leonardo, Deceglie, Michael G., and Muller, Matthew. Predicting photovoltaic soiling losses using environmental parameters: An update. United States: N. p., 2018. Web. doi:10.1002/pip.3079.
Micheli, Leonardo, Deceglie, Michael G., & Muller, Matthew. Predicting photovoltaic soiling losses using environmental parameters: An update. United States. doi:10.1002/pip.3079.
Micheli, Leonardo, Deceglie, Michael G., and Muller, Matthew. Tue . "Predicting photovoltaic soiling losses using environmental parameters: An update". United States. doi:10.1002/pip.3079.
@article{osti_1480682,
title = {Predicting photovoltaic soiling losses using environmental parameters: An update},
author = {Micheli, Leonardo and Deceglie, Michael G. and Muller, Matthew},
abstractNote = {This study presents an investigation on the correlations between soiling losses and environmental parameters at 41 locations in the United States, with the aim of analyzing the possibility of predicting soiling losses at a site even when soiling data are not available. The results of this work, which considers the largest pool of soiling data points systematically investigated so far, confirm that a single-variable regression based on particulate matter concentration returns the best correlations with soiling, with adjusted coefficients of determination up to 70%, corresponding to RMSE as low as 0.9%. Among the various particulate matter datasets investigated, a gridded Environment Protection Agency dataset is for the first time found to return correlations similar to those obtained by interpolating particulate matter monitoring station data. We discuss in detail the different interpolation techniques used to process the particulate matter concentrations because they can greatly impact the correlations. Specifically, the correlation coefficients between soiling and particulate matter range between 70% and less than 20%, depending on the interpolation methods and monitoring distance. Spatial interpolation methods based on inverse distance weighting are found to return better correlations than a nearest neighbor or a simple average approach, especially when large distances are considered. Similarly, the effects of different rain thresholds used to calculate the length of the dry periods are examined. An enhanced two-variable regression is found to achieve higher-quality correlations, with adjusted R2 of 90% (RMSE = 0.55%), also suggesting that high and low soiling locations might be differentiated depending on fixed particulate matter or rainfall thresholds.},
doi = {10.1002/pip.3079},
journal = {Progress in Photovoltaics},
issn = {1062-7995},
number = none,
volume = 2018,
place = {United States},
year = {2018},
month = {10}
}