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%.

Mapping photovoltaic soiling using spatial interpolation techniques / Micheli, L.; Deceglie, M. G.; Muller, M.. - In: IEEE JOURNAL OF PHOTOVOLTAICS. - ISSN 2156-3381. - 9:1(2019), pp. 272-277. [10.1109/JPHOTOV.2018.2872548]

Mapping photovoltaic soiling using spatial interpolation techniques

Micheli L.
;
2019

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%.
2019
map; photovoltaic (PV) systems; soiling; spatial interpolation
01 Pubblicazione su rivista::01a Articolo in rivista
Mapping photovoltaic soiling using spatial interpolation techniques / Micheli, L.; Deceglie, M. G.; Muller, M.. - In: IEEE JOURNAL OF PHOTOVOLTAICS. - ISSN 2156-3381. - 9:1(2019), pp. 272-277. [10.1109/JPHOTOV.2018.2872548]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1625159
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