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Contract No. DE-AC36-08GO28308

A Method to Extract Soiling 

Loss Data from Soiling Stations 

with Imperfect Cleaning 

Schedules

Preprint

Matthew Muller

National Renewable Energy Laboratory

Leonardo Micheli

National Renewable Energy Laboratory and Colorado 

School of Mines

Alfredo A. Martinez-Morales

University of California, Riverside

Presented at the 44th IEEE Photovoltaic Specialists Conference

Washington, D.C.

June 25–30, 2017

Suggested Citation:

Muller, M., L. Micheli, and A.A. Martinez-Morales. 2017. “A Method to Extract 

Soiling Loss Data from Soiling Stations with Imperfect Cleaning Schedules: 

Preprint.” Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5J00-67225. https://www.nrel.gov/docs/fy18osti/68225.pdf.

Conference Paper

NREL/CP-5J00-68225

June 2018
NOTICE

This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for 

Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. 

Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy 

Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the 

U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, 

acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to 

publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

This report is available at no cost from the National Renewable 

Energy Laboratory (NREL) at www.nrel.gov/publications.

U.S. Department of Energy (DOE) reports produced after 1991 

and a growing number of pre-1991 documents are available 

free via www.OSTI.gov.

Cover Photos by Dennis Schroeder: (left to right) NREL 26173, NREL 18302, NREL 19758, NREL 29642, NREL 19795.

NREL prints on paper that contains recycled content.
1

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A Method to Extract Soiling Loss Data From Soiling Stations with 

Imperfect Cleaning Schedules

Matthew Mullera, Leonardo Michelia,b, and Alfredo A. Martinez-Moralesc

a National Renewable Energy Laboratory, Golden, Colorado, 80401, United States

b Colorado School of Mines, Golden, Colorado, 80401, United States

c University of California, Riverside, California, 92507, United States

Abstract — Typical PV soiling stations determine the natural 

soiling losses by comparing the output of a naturally soiled PV cell 

to that of a PV cell maintained in the clean state. Inadequate 

cleaning frequency, equipment failure, or human error provides 

opportunity for the cleaned cell to soil, directly resulting in error 

in the reported soiling ratio. This work investigates an algorithm 

to automatically detect and correct the data stream for errors 

associated with soiling of the clean cell. The methodology is tested 

on several soiling stations with irregular cleaning schedules as well 

as two soiling stations where both ideal and imperfect cleaning 

schedules are in place. The initial results show that the algorithm 

can reduce error associated with imperfect cleaning but also 

confirms the benefits of maintaining an optimal cleaning schedule.

I. INTRODUCTION

PV soiling losses (energy generation losses due to dust on a

PV surface) can be in the range of 0-6% in the United States 

and can be 20-80% in dusty regions of the world like the Middle 

East [1]. Lost energy generation directly equates to lost dollars 

and therefore as the PV market has expanded into the dusty 

regions of the world, soiling research has increased 

dramatically. Soiling research spans a range of topics including:

soiling loss measurements and associated equipment, dust 

characterization, soiling adhesion mechanisms, soiling loss 

modeling, automated cleaning systems, and module surface 

coatings to reject soil adhesion. This work seeks to add to the 

PV community’s ability to measure and report soiling loss data 

so the data are both reliable and well understood. Soiling 

stations are being used around the world to report soiling losses 

which are then used by various parties such as financiers, 

performance modelers, and planners of system operations and 

maintenance (O&M). While soiling stations can vary in design

(i.e. cell versus module measurements, short-circuit current 

versus peak power tracking, tilt angle, and choices on 

supplementary meteorological equipment), in most stations the 

soiling loss measurement is the ratio of the output of a naturally 

soiled PV device to the output of a clean PV device. This type 

of device is well described in the literature; including 

measurement uncertainty associated with errors in mounting, 

calibration drift, angle of incidence effects, and temperature 

effects [2-5]. The fundamental measured ratio between a dirty 

and clean PV device, however, is not always systematically 

treated. While the dirty device is allowed to soil based on 

natural events, the cleaned cell is maintained in the “clean” state 

by manual or automated cleaning practices. The authors of this 

work have experienced a range of soiling stations where the 

“clean” state is assumed because daily, weekly, biweekly, or 

other irregular cleaning intervals are applied to the clean cell.

Although “clean” in the purest sense would imply no soil 

particles on the PV device, this is not realistic for any outdoor 

device. Rather, the amount of soiling allowed on the clean cell 

impacts the uncertainty of the reported soiling ratio. The level 

of tolerable uncertainty will generally depend on the end needs 

of the data user and therefore are not defined in this work. On 

the other hand, if nothing can be established in regards to the 

uncertainty of an imperfect soiling ratio (it could be in the range 

of 0-100% losses), the data are of no use. Therefore it is 

valuable to be able to somehow quantify the uncertainty of an 

imperfect soiling ratio and develop methods to glean valuable 

information from this data. Hereafter, a methodology is 

presented to quantify errors in an imperfect soiling ratio, correct 

for these errors, and quantify the uncertainty based on these 

errors.

II. METHODS FOR SOILING LOSS EXTRACTION

A. Soiling Stations and soiling metrics

This study is based on the data recorded by ten soiling 

stations installed in the southwestern United States. Each 

soiling station has two identical PV cells, where one is intended 

to be the clean device and the other is allowed to naturally soil.

The ten sites were chosen because the cleaning was done 

manually and the intervals of cleaning were not consistently 

achieved. Using the same method employed in [3], a daily 

soiling ratio (SRatio) is calculated, obtained as the ratio 

between the daily mean short circuit currents of the two cells. 

A soiling ratio of one represents clean conditions, and its value 

decreases with the soiling. Short circuit currents, irradiances 

and weather data are recorded each minute and converted into 

hourly data. Only data occurring between 11AM and 1PM have 

been considered to minimize variation due to angle of incidence 

effects. Performance plots of each soiling station have been 

visually checked in order to remove any data due to equipment 

malfunction, shading, or performance outside of reasonable 

bounds.

B. Algorithm for correcting imperfect soiling ratio data

An algorithm has been developed to correct for errors in the 

measured soiling ratio due to infrequent cleaning and therefore 

soiling of the clean cell. Fig. 2 provides a flow chart of the 
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algorithm where key points are as follows: 1) Noise is defined

as two times the mean of all upward shifts in the SRatio not 

associated with precipitation, 2) Downward shifts in the SRatio 

that are twice or more than the noise are interpreted as a 

cleaning of the clean cell and the SRatio following the shift is 

an accepted data point. 3) In cases where precipitation coincides 

with an upward shift in the SRatio (twice or more than the 

noise) both cells are considered clean and the SRatio is set to 

one, 4) SRatios between acceptable data points are determined 

by connecting lines between the acceptable data points(note the 

majority of measured data points are rejected). Fig. 1 is a 

cartoon of soiling station data that helps to elucidate these key 

points. The black SRatio points demonstrate measurements of 

the SRatio over 50 days. The SRatio starts at one because both 

cells have been cleaned and then it has a noisy trend until day 

14 where a downward shift is detected. The red triangle is 

considered a true measurement of the SRatio and a line is drawn

between day 1 and day 14. On day 30 an upward shift and rain 

are detected so the SRatio is set to one. The SRatio 

measurement on day 29 was not acceptable because of 

unknown soil level on the clean cell (i.e. no downward shifts 

were detected beyond day 14). The SRatio slope between day 1 

and day 14 is projected to day 29 because it is the most recent 

estimate of what is occurring at the site. No shifts are detected 

between the rains on day 30 and day 50 and so no new 

information is obtained about the rate of change in the SRatio. 

An estimate is then assumed by using the slope that was used 

in the previous soiling period. This assumes that both cells soil 

together between days 30 and 50 and that the 14 day cleaning 

schedule was missed. It is possible that soiling could have been 

minimal in this time period but the authors considered it more 

conservative to assume soiling is occurring.

Fig. 1. A cartoon of key points in the algorithm for correcting an 

imperfect soiling ratio.

Fig. 2. A flow chart that explains the steps taken in the corrective 

algorithm.

Fig. 3 presents an example of how the SRatio correction 

algorithm is applied to an actual soiling station in the southwest 

United States. The black data points are the measured SRatio 

data and red lines connect each set of data points that the 

algorithm accepts. Days with precipitation are marked in blue 

and the downward shift detection is marked in green. The 

period from July through October shows a series of downward 

steps that are believed to be cell cleanings about every two 

weeks. The fact that the steps are easily visible indicates that 

significant soiling is possible at this site on the clean cell over 

two weeks. This site is an ideal case for the correction algorithm 

because the dry period is multiple months and data points every 

two weeks make it possible to still see the longer downward 

trend.
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TABLE I

AVERAGE SOILING RATIOS DETERMINED FOR 10 SITES IN THE SOUTHWESTERN UNITED STATES. THE RATIOS ARE GIVEN FOR THE 

MEASURED VALUES (SRM) AND BASED ON THE CORRECTIVE ALGORITHM (SRC). ALL VALUES ARE GIVEN AS PERCENTAGES

SITE 1 2 3 4 5 6 7 8 9 10

SRM 99.9 99.6 99.8 95.9 99.2 99.1 93.6 99.4 99.9 99.9

SRC 99.5 99.1 98.8 95.4 98.8 97.5 91.9 99.5 99.4 99.4

U 0.2 0.6 0.9 1.9 1.7 2.1 3.3 0.4 0.6 0.6

Fig. 3. Applying the correction algorithm to the measured SRatio

for one site in the southwest United States.

B. Uncertainty determination

A major assumption of the SRatio correction algorithm is that 

the soiling rate (slope of the SRatio) is linear between accepted 

data points. It is possible that over an interval like two weeks 

that soiling rate could vary for many reasons (i.e. pollution from 

a worksite, and agricultural activity or changes in site 

conditions). Without actually measuring such deviations one 

cannot quantify the specific effects but errors in the SRatio are 

likely to occur that are both positive and negative (therefore 

canceling each other in an average result). A data set is being 

collected to analyze such variation but the magnitude of shifts 

in the SRatio at each cleaning also provide information about 

the worst case uncertainty for the average SRatio. For example,

if after two weeks the clean cell is cleaned and the SRatio shifts 

by 10%, the worst case scenario is that a large soiling event 

occurred immediately after the previous cleaning and the 

SRatio was in 10% error for the entire 2 weeks. It is unrealistic 

to assume this would always happen and therefore the 

magnitudes of all the detected downward shifts are averaged 

and this value is reported as the site uncertainty. A long term 

data set with side-by-side PV cells cleaned at different 

frequencies would provide a more thorough uncertainty 

analysis but the above value is useful because it is conservative 

(the most extreme soiling loss case is estimated) and it will 

increase both for dirtier sites and for longer periods between 

cleanings. The reported uncertainty therefore provides a 

measure of the impact of less frequent cleanings.

III. RESULTS AND DISCUSSION

A. Results for average soiling ratios

Table I provides the average SRatios for the 10 sites in this 

study. SRM is the average of all the daily measured SRatios 

while SRC is average of the daily values per the corrective 

algorithm. The site uncertainty, U, as described in the previous 

section, is also given in Table I.

B. Discussion

The results in Table I show that the 10 sites have measured 

soiling ratios from 93.6% to 99.9% and the corrected values 

range from 91.9% to 99.5%. Some sites that have measured 

losses less than 1% have corrections on the order of tenths of a 

percent, while others have corrections on the order of a few 

percent. Site 6 has the greatest correction, reducing from 99.1% 

to 97.5%. While the model has yet to go through a true 

validation, the fit in Fig. 3 suggests that it is possible to 

automatically detect soiling station cleaning events and then use 

these events to construct a correction for a long term soiling 

trend. It is interesting to note that the uncertainty values as 

defined in the previous section match very closely to the 

magnitude of the correction for each site. Although the 

algorithm and uncertainty are related to each other, the 

algorithm applies a series of complex steps and assumptions 

whereas the uncertainty represents only the average of all the 

errors in the SRatio for just the days where the SRatio shifts 

downward (believed to be cleanings of the clean cell).

C. Validation efforts

In order to draw further conclusions on the value and 

uncertainty of the proposed SRatio corrective algorithm, two

soiling stations were built and recently deployed to gather 

validation data. Fig. 4 shows a picture of one of the validation 

stations which each have four PV cells. One of the cells is left 

to naturally soil while the others are intended to be cleaned 

daily, weekly and every other week. One of the stations was 

deployed at the University of California, Riverside where past 

years have shown an average annual soiling losses of ~ 4-6%

[6]. The second station was employed in Golden, Colorado 

where the average annual soiling loss is typically less than 1%. 
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Due to this low soiling loss in Golden, artificial soiling was 

employed to increase the soiling rate. A leaf blower was used 

on an approximate daily basis to agitate soil and other matter 

on the ground adjacent to the soiling station, effectively 

creating a short term dust cloud around the soiling station. The 

Golden station was intended for a short term proof of concept 

while the Riverside station was intended to be an actual field 

validation. 

Fig. 4. Deployed PV cells cleaned daily, weekly, every 2 weeks, 

and not at all to validate the results of the correction algorithm.

Golden Station

The intent of the Golden station was to provide a first pass 

validation of the correction algorithm while waiting for longer 

term data from a field site with substantial soiling. Although 

some lessons were learned from the Golden station the data did 

not provide a simple first pass validation. The artificial soiling 

station presented the following challenges and lessons:

1) Leaf blower soiling resulted in losses on the order of 

several percent per day (greater than any typical U.S. location).

2) Leaf blower soiling rates were not as linear as what is often 

seen in the field and the SRatio seemed to bottom out near 0.5.

3) Precipitation frequency was on the order of every 2 weeks 

and therefore prevented obtaining a useful comparison between 

weekly and biweekly cleaning.

4) The majority of precipitation was in the form of snow. The 

typical experience with PV systems at NREL is that, on the first 

sunny day following a snow, the solar panels will be warmed 

and the snow will slide off all of the panels in a matter of 

minutes, resulting in a complete cleaning. Although the soiling 

station reference cells were mounted at latitude tilt, the cells did 

not behave the same in response to snow as full size panels. On 

some days following snowstorms, the snow was seen to slowly 

melt and partially slide on the small reference cell. The data 

showed that, in several cases, this resulted in partial cleaning 

over several days (the SRatio increases over multiple days 

following precipitation, see the yellow circle on Fig. 5). The 

corrective algorithm was not designed to correct for partial 

cleanings or for cleanings that occur over multiple days due to 

melting snow and therefore it performed poorly in these time 

periods.

Fig. 5 provides the time series SRatio data for the daily and 

weekly cleanings as well as the result of applying the algorithm 

to the weekly cleaning data. The actual average measured 

SRatio per the daily cleaning was 0.82, the uncorrected average 

from weekly cleaning was 0.86, and the result from applying 

the corrective algorithm was 0.78. The soiling loss was over 

estimated during the snow melt off periods and therefore the 

corrective algorithm over predicted the soiling losses and 

therefore the corrective algorithm provides no clear benefit.

Riverside Station

At the time of preparation of this paper data was only 

available from the Riverside station from 3/6/2017 through 

5/9/2017. Students provided the cleaning support and therefore 

cleaning did not occur on the weekends or during spring break 

(3/25/2017-4/3/2017). Further issues resulted in no cleaning 

occurring from 4/8/2017 – 4/25/2017. Although this results in 

only approximately 7 weeks of data where the true soiling ratio 

was measured (daily cleaning), the corrective algorithm was 

still run on various data streams and the results for the biweekly 

cleaning are shown in Fig. 6. 

Fig. 5. Golden, Colorado soiling station with artificial soiling. The 

soiling ratio based on daily and weekly cleaning is shown along with 

the soiling ratio correction algorithm as applied to the weekly cleaning 

data. The corrective algorithm is shown as a series of black lines that 

connect all the black dots. The black dots align with the dates for 

which there are also red dots. The red dots are values where shifts are 

detected which are used to construct the corrected SRatio. In February

(yellow circle) there were several snow events (snow dates not shown 

on the graph) that caused partially cleaning and therefore the true 

SRatio (daily cleaning) improves. Although upward shifts were 

recorded by the algorithm (red dots) the snow did not occur on the days 

of the upward shifts and therefore the algorithm fits with a zero slope. 

The cleaning by snow usually occurs in the days following the snow 

storm. No changes were made to account for the effects of snow 

because typical high soiling sites are desert environments where snow 

is not expected to be a factor.

Although the data collection period is quite short, the graph 

clearly demonstrates that cleaning every two weeks in 

Riverside does not result in an accurate SRatio. The first three 

periods between cleanings result in a SRatio that trends upward. 

This suggests the clean cell actually soils at a slightly faster rate 

than the cell that is only cleaned by rain or natural events. The 

average SRatios (for the entire data collection period) for the 

different cleaning frequencies are as follows: estimate of the 

“true” SRatio = 0.98 (daily data with corrections applied for the 

noted time period where no cleanings were performed), SRatio 
5

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measured using weekly cleaning = 0.99, SRatio after applying 

a correction to the weekly cleaning data = 0.98, SRatio 

measured using biweekly cleaning = 0.99, SRatio after 

applying a correction to the biweekly cleaning data = 0.98. The 

corrective algorithm is successful in predicting the estimate of 

the “true” SRatio based on both weekly and biweekly cleaning 

cells but caution must be taken in using these results for the 

following reasons: 1) The “true” value is only an estimate due 

to the weeks where cleaning support was not available, 2) The 

data only span 9 weeks and therefore should be considered 

preliminary, and 3) No extended soiling period has been 

experienced and therefore the soiling losses are generally low 

regardless of the cleaning frequency.

Fig. 6. The soiling ratio measurements based on the biweekly 

cleaned cell are shown in comparison to the estimate of the true soiling 

ratio and the correction as applied to the data from biweekly cleaning. 

A dashed line is shown where the true soiling ratio was estimated as 

daily cleaning did not occur in this time period. The data from 

biweekly cleaning illustrates the problem with infrequent cleanings. 

The trend of the soiling ratio appears to be positive between cleanings. 

There has been anecdotal evidence that a regularly manually cleaned 

cell can soil at a slightly different rate than a cell that is only cleaned 

by rain but the trend in this graph could also be a product of noise.

IV. PRELIMINARY CONCLUSIONS

Soiling stations are being deployed all over the world to 

determine information about the soiling losses on PV systems.

These soiling stations provide a SRatio between a dirty and 

cleaned PV device but often the SRatio has significant error 

because the cleaned cell is also allowed to soil, depending on 

the cleaning interval. If no information is known about the level 

of soiling on the clean cell, then the true SRatio is also 

unknown. An algorithm has been presented that automatically 

detects cleaning events for the clean cell and therefore 

establishes trustworthy data points in the time series data set for 

the SRatio. These data points have been used to both determine

a worst case uncertainty for the annual average measured 

SRatio and to estimate a time series correction for the SRatio.

As should be expected, the uncertainty for a site increases when 

the soiling rate is greater and the time between cleanings is 

greater. The uncertainty metric ultimately provides a way to 

interpret and use data that otherwise is difficult to trust.

An initial effort was presented to validate the proposed 

SRatio corrective algorithm based on an outdoor artificial 

soiling station in Golden, Colorado and a representative field 

soiling station in Riverside, California. The Golden soiling 

station presented several limitations but it demonstrated that the 

corrective algorithm failed to appropriately handle partially 

cleanings that occur when snow melts on the surface of PV 

reference cells. This is not surprising as the algorithm was 

designed around complete cleanings; either manually 

performed or due to significant rainfall. Validation based on 

the station in Riverside, California is very limited due to the 

short data collection period. The initial results from the 

Riverside station suggest that the corrective algorithm has 

potential for sites when cleaning events result in full, rather than 

partial, cleaning. Both weekly and biweekly cleaning of the 

clean cell underestimated the losses and the algorithm was able 

to correct for this. Further validation efforts are necessary to 

determine if the corrective algorithm should be more broadly 

used.

ACKNOWLEDGEMENTS

The authors would like to thank Ryo Huntamer and Aaron 

Chan for their support installing and maintaining the soiling 

station at the University of California, Riverside.

This work was supported by the U.S. Department of 

Energy under Contract No. DE-AC36-08GO28308 with the

National Renewable Energy Laboratory. The U.S. Government 

retains and the publisher, by accepting the article for 

publication, acknowledges that the U.S. Government retains a 

nonexclusive, paid up, irrevocable, worldwide license to 

publish or reproduce the published form of this work, or allow 

others to do so, for U.S. Government purposes.

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