Mapping Photovoltaic Soiling Using Spatial Interpolation Techniques
Abstract
In this paper, we present a new soiling map developed at the National Renewable Energy Laboratory, showing data from 83 sites in the United States. Soiling has been measured through soiling stations or extracted by photovoltaic system performance data using referenced techniques. The data on the map have been used to conduct the first regional analysis of soiling distribution in the United States. We found that most of the soiling occurs in the southwestern United States, with Southern California counties experiencing the greatest losses because of the high particulate matter concentrations and the long dry periods. Moreover, we employed five spatial-interpolation techniques to investigate the possibility of estimating soiling at a site using data from nearby sites. We found that coefficients of determination of up to 78% between estimated and measured soiling ratios, meaning that, by using selective sampling, soiling losses can be predicted using the data on the map with a root-mean-square error of as low as 1.1%.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- National Renewable Energy Lab. (NREL), Golden, CO (United States); Univ. de Jaen (Spain); Leidos, Denver, 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:
- 1479873
- Report Number(s):
- NREL/JA-5K00-71624
Journal ID: ISSN 2156-3381
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- IEEE Journal of Photovoltaics
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 2156-3381
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; 29 ENERGY PLANNING, POLICY, AND ECONOMY; map; photovoltaic (PV) systems; soiling; spatial interpolation
Citation Formats
Micheli, Leonardo, Deceglie, Michael G., and Muller, Matthew. Mapping Photovoltaic Soiling Using Spatial Interpolation Techniques. United States: N. p., 2018.
Web. doi:10.1109/JPHOTOV.2018.2872548.
Micheli, Leonardo, Deceglie, Michael G., & Muller, Matthew. Mapping Photovoltaic Soiling Using Spatial Interpolation Techniques. United States. https://doi.org/10.1109/JPHOTOV.2018.2872548
Micheli, Leonardo, Deceglie, Michael G., and Muller, Matthew. 2018.
"Mapping Photovoltaic Soiling Using Spatial Interpolation Techniques". United States. https://doi.org/10.1109/JPHOTOV.2018.2872548. https://www.osti.gov/servlets/purl/1479873.
@article{osti_1479873,
title = {Mapping Photovoltaic Soiling Using Spatial Interpolation Techniques},
author = {Micheli, Leonardo and Deceglie, Michael G. and Muller, Matthew},
abstractNote = {In this paper, we present a new soiling map developed at the National Renewable Energy Laboratory, showing data from 83 sites in the United States. Soiling has been measured through soiling stations or extracted by photovoltaic system performance data using referenced techniques. The data on the map have been used to conduct the first regional analysis of soiling distribution in the United States. We found that most of the soiling occurs in the southwestern United States, with Southern California counties experiencing the greatest losses because of the high particulate matter concentrations and the long dry periods. Moreover, we employed five spatial-interpolation techniques to investigate the possibility of estimating soiling at a site using data from nearby sites. We found that coefficients of determination of up to 78% between estimated and measured soiling ratios, meaning that, by using selective sampling, soiling losses can be predicted using the data on the map with a root-mean-square error of as low as 1.1%.},
doi = {10.1109/JPHOTOV.2018.2872548},
url = {https://www.osti.gov/biblio/1479873},
journal = {IEEE Journal of Photovoltaics},
issn = {2156-3381},
number = 1,
volume = 9,
place = {United States},
year = {Fri Oct 12 00:00:00 EDT 2018},
month = {Fri Oct 12 00:00:00 EDT 2018}
}
Web of Science
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