NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Pyranometers and Reference 

Cells: Part 2: What Makes the 

Most Sense for PV Power 

Plants?

Preprint

Jenya Meydbray

PV Evolution Labs

Evan Riley

Black and Veatch

Lawrence Dunn

Atonometrics

Keith Emery and Sarah Kurtz

National Renewable Energy Laboratory

To be published in PV Magazine

Journal Article

NREL/JA-5200-56718

October 2012
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1 

Part 2 of 2 

Pyranometers and Reference Cells: Part 2: What

Makes the Most Sense for PV Power Plants?

Jenya Meydbray1, Evan Riley2, Lawrence Dunn3, Keith Emery4, and Sarah Kurtz4 

1

PV Evolution Labs, 1360 Fifth Street, Berkeley, CA 94710 

2

Black and Veatch, 650 California Street, San Francisco, CA 94108 

3

Atonometrics, 8900 Shoal Creek Blvd., Suite 116, Austin, TX 78757 

4

National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401 

As described in Part 1 of this two-part series, thermopile pyranometers and photovoltaic (PV) 

reference cells can both be used to measure irradiance; however, there are subtle differences 

between the data that are obtained. This two-part article explores some implications of 

uncertainty and subtleties of accurately measuring PV efficiency in the field. Part 2 of the series 

shows how reference cells can be used to more confidently predict PV performance, but how this 

could best be accomplished if historic irradiance data could be available in PV-technologyspecific formats.

Globally, equity investors (owners) and debt providers (lenders) for PV power plants evaluate 

projects based on expected return on investment, perceived risk, and the magnitude of the capital 

cost. Projects are typically financed with a combination of debt and equity. From a capital cost 

perspective it is often best for the project to maximize the amount of debt that the project incurs. 

This is because debt typically has a lower capital cost than equity. The amount of debt a project 

can incur is a function of size, risk, and variability of the expected project revenues. By reducing 

the risk that project revenues will be lower than expected it is possible that the project will be 

able to take on more debt, and therefore reduce the working capital cost of the owner. This 

translates into a plant with a higher rate of return and a lower Levelized Cost of Energy (LCOE). 

In specific cases it is possible that reducing the uncertainty in the solar resource by 

approximately 1 percent will allow the project to take on approximately 1 percent more debt. 

This may seem insignificant for a 30-kW project that is valued at $120,000 as this reduction in 

uncertainty translates into increasing the loan size by approximately $1,200. However, for a 30-

MW project valued at $120,000,000 this reduction in uncertainty can translate into increasing the 

loan size by approximately $1,200,000. 
2 

Performance Measurement: Can Reference Cells Reduce the 

Uncertainty?

When measuring the performance of a power plant the two considerations are: 

1. Fuel In (useable sunlight) 

2. Electricity Out (kilowatt-hours) 

Commercially available revenue grade AC kWh meters measure the electricity out with high 

precision (0.2% or better) [1]. This article focuses on the measurement of fuel in, or sunlight 

available to the solar cells for energy conversion. Useable sunlight is defined as the light 

incident upon a solar cell that can be converted into electricity. Spectral components of light that 

can’t be converted by solar cells (e.g. infrared light) and light that is reflected from the glass are 

not considered useable sunlight. 

PV power plants are a variable electricity source. In other words, as the weather varies the 

output of the PV system changes. This is caused by variations in the temperature, sunlight 

intensity, wind, spectral shifts of irradiance, the angle of incidence between the sun and the PV 

modules, the ratio of diffuse to direct light, and several other factors. PV modules do not respond 

to all colors of light equally and, thus, have some non-flat spectral response as seen in Figure 1. 

The spectrum of the incident sunlight changes with time of day, time of year, location, albedo

and local atmospheric aerosol and moisture content. Additionally, PV modules are constructed 

with flat glass whose reflectance is a function of sunlight incidence angle.

Solar reference cells that utilize the same bill of materials (cell, glass, encapsulant, backsheet, 

etc.) as the PV modules comprising the PV power plant will exhibit a matching response with 

respect to spectrum and angle of incidence. In contrast to reference cells, thermopile 

pyranometers are constructed with a black disc covered by a single or double glass or quartz 

dome. The disc absorbs all sunlight that is transmitted through the dome. In part due to the air 

gap between the black disk and the glass or quartz dome, pyranometers exhibit a unique angle of 

incidence response. Pyranometers also exhibit a flat spectral response to all incident photons 

passing through the dome, as seen in Figure 1. In other words, they respond to the energy in all 

colors of light transmitted through the dome equally. Because of this, pyranometers measure 

broadband incident sunlight, which is similar to, but not exactly the same as useable fuel for the 

solar cells. Pyranometers may also exhibit a response to diffuse light that differs from PV 

devices, introducing additional measurement differences on cloudy days. 
3 

Figure 1. Spectral response (right axis) of several PV technologies and of a thermopile 

pyranometer. The spectral irradiance (left axis) of the standard global-irradiance solar 

spectrum is also shown.

Variations in efficiency over time can occur because of manufacturing defects in any of the PV 

system components. For example, if the PV module develops a poor electrical connection, the 

associated resistive losses will cause greater reduction in efficiency at high-irradiance conditions. 

The emergence of localized shunts in the solar cell will result in reduced low-light performance. 

The challenge of identifying whether the PV manufacturer meets the terms of the contract is in 

accurately quantifying if the efficiency varies with the weather as expected. Thus, the ideal 

irradiance sensor for monitoring PV performance would also vary with the weather in a way that 

mimics how a “good” or “defect-free” PV product is expected to vary. In other words, if one can 

accurately measure the total useable incident irradiance under any environmental condition (fuel 

in) by using a matched (i.e., PV) reference device, one can determine if the PV system is 

generating electricity per expectations (energy out).

Two of the authors recently published an uncertainty analysis of irradiance measurement for PV 

power plant performance assessment [2]. That work evaluated the uncertainty in typical 

measurements of the useable fuel available to a PV system as measured with a thermopile 

pyranometer and PV reference cell. 

The uncertainty calculations were performed for a fixed-tilt system on a single, clear day, and 

assumed simultaneous, side-by-side measurements with a PV reference device and a thermopile 

pyranometer. Although a full explanation of those results is beyond the scope of this paper, we 

show in Tables 1 and 2 a snapshot of the uncertainty analysis corresponding to a PV-matched 

irradiance intensity of 1000 W/m2

. In both tables the major sources of uncertainty are first listed

as standard uncertainties, which may be thought of as a combination of random and non-random 

error sources. The standard uncertainties are combined using a root sum-of-squares method, and 

the total expanded uncertainties, corresponding to confidence intervals of 95% in the measured 

quantity, are calculated by multiplying the combined, total standard uncertainties by a coverage 

factor, k, of ~2. 
4 

Table 1. Constituent standard uncertainties and combined expanded uncertainty associated with 

typical thermopile pyranometer measurements of useable fuel for PV systems. The uncertainties 

shown here correspond to clear sky conditions at 1000 W/m2

. 

Pyranometer Responsivity Standard Uncertainty 2.69%

Pyranometer Measured Voltage Standard Uncertainty 0.23%

Total Standard Uncertainty (Root Sum of Squares) 2.70%

Total Expanded Uncertainty (95% Confidence Interval) 5.29%

In Table 1 the two standard uncertainties listed correspond to the responsivity of the 

pyranometer, R, which relates the pyranometer output voltage, V, to the measured irradiance 

through the expression Irradiance = V/R, and the uncertainty in the voltage measurement of the 

pyranometer’s output. The major contributors to the uncertainty in the pyranometer responsivity 

are the calibration of the pyranometer (taken from a calibration certificate), the spectral 

mismatch of the pyranometer with respect to PV devices, the angular response of the 

pyranometer, and the thermal offset of the pyranometer. Smaller contributions to the uncertainty 

in pyranometer responsivity are made by the variation in pyranometer output with temperature, 

the angular alignment error between the pyranometer and the plane of array, and the nonlinearity 

in the pyranometer’s output with irradiance intensity.

Table 2. Constituent standard uncertainties and combined expanded uncertainty associated with 

typical PV reference cell measurements of useable fuel for PV systems with a response that 

matches the PV reference device. The uncertainties shown here correspond to clear sky 

conditions at 1000 W/m2

. 

PV Ref Device Isc Calibration Value Standard Uncertainty 1.11%

PV Ref Device Isc Measurement Standard Uncertainty 0.39%

Isc Temperature Coefficient Standard Uncertainty 0.39%

Measured Device Temperature Standard Uncertainty 0.09%

Total Standard Uncertainty (Root Sum of Squares) 1.24%

Total Expanded Uncertainty (95% Confidence Interval) 2.44%

In Table 2, the PV reference cell Isc calibration value uncertainty is taken from a calibration 

certificate. The major contributors to the uncertainty in the measurement of Isc are the 

measurement electronics, the nonlinearity of the PV reference cell Isc with irradiance, and the 

angular alignment error between the PV reference cell and plane of array. It is assumed that the 

reference and test devices have identical spectral responses, allowing the uncertainty in the 

spectral correction factor to be neglected. The major contributors to the PV reference cell 

temperature measurement are due to the temperature sensor, the temperature transducer, and, if 

the temperature sensor is mounted on the back of the cell package, the difference between the 

reference cell package back temperature and the cell junction temperature. The uncertainty 

associated with temperature may be larger in climates with extreme temperatures.

The expanded uncertainties shown in Tables 1 and 2 should be thought of as the intervals about 

the measured effective irradiance within which the true value of the effective irradiance is 

believed to lie with a confidence of 95%. From Tables 1 and 2 we see that under clear sky 
5 

conditions typical irradiance measurement uncertainty intervals are on the order of ±5.3% for 

thermopile pyranometers, and ±2.4% for PV reference cells. For a more complete explanation of 

these results we refer the reader to Reference [2], and the references contained therein. 

The uncertainties listed in Table 1 correspond to high-quality thermopile pyranometers under 

clear-sky conditions. For thermopile pyranometers, measurement uncertainties may be 

significantly higher if a lower quality instrument is used (e.g., a second-class pyranometer), or if 

atmospheric conditions differ from the clear-sky conditions assumed. The uncertainties listed for 

PV reference cell irradiance measurements in Table 2 should be relatively easy to obtain as long 

as the PV reference cell is constructed from a stable, single junction (e.g., silicon) cell in a 

suitable package with a matched spectral response. In principle, PV matched reference cell 

measurement uncertainties of useable fuel for PV systems should not change with atmospheric 

conditions [3]. In all cases, the uncertainties listed assume that the measurements are 

implemented by a skilled individual using standard calibration protocols. (Footnote: Calibration 

of the sensors may be done at both the beginning and end of the test to check for any drift in the 

sensor calibration. Pyranometers may drift either up or down; a matched reference cell could 

degrade at a rate similar to that of the overall system, but typical PV degradation rates are ~ 

0.5%/y or less, which is likely to be less than the uncertainty of the calibration.)

We note that an additional source of error not included in our analysis above is due to the time 

response of the pyranometer, which is much slower than the response of a reference cell. If it is 

desired to obtain instantaneous agreement between the predicted and measured output of a PV 

system, then the reference cell provides a more accurate response. However, given that the 

pyranometer may be sampling the conditions for a large area around it and that the performance 

guarantee usually applies more to the long-term performance than to the instantaneous 

performance, the difference in time response may have little impact in the choice between the 

two for performance guarantees. We therefore have not included in our analysis errors attributed 

to the varying time responses of the two devices. 

The emphasis in this work is on measuring the amount of useable fuel available to a PV system, 

not on measuring the “total” or broadband amount of solar radiation. As a result, the analysis 

summarized in Tables 1 and 2 attributes sources of uncertainty such as spectral and angle of 

incidence errors to irradiance measurements made with pyranometers. After all, the PV module 

manufacturer is in a good position to guarantee the PV module efficiency with respect to a 

reference spectrum as given in Reference [4] but, obviously, is not in control of the local spectral 

distribution or the weather. Similarly, an EPC firm responsible for commissioning a PV power 

plant can’t control weather effects that could lower the apparent efficiency of a PV power plant 

when calculated using a broadband irradiance measurement made with a thermopile 

pyranometer. These effects can be separated from performance assessments of PV modules by 

using matched PV reference cells for irradiance measurements. Nevertheless, when predicting 

the performance of a PV project, someone must bear the risk associated with weather variability.

The wide availability of broadband irradiance data for locations throughout the world facilitates 

prediction of performance of new PV projects. For example, Typical Meteorological Year 

(TMY) data are available for hundreds of sites; similarly, other datasets are available. In a 

minority of cases, site-specific irradiance data may be available, but for the purposes of 

discussion, we will focus on the common approach of using TMY-type data.
6 

For a given system design and appropriate TMY data set, a prediction of the PV performance can 

be obtained using software such as PVSyst [5] or SAM [6]. The calculation involves multiple 

steps, but these may be simplified into (see Fig. 3): 

1. Translate the broadband irradiance data from horizontal or other surface to Plane-OfArray (POA) irradiance, 

2. Estimate the expected temperature of the PV modules from the TMY conditions and the 

module properties, and 

3. Predict the PV performance based on the POA irradiance and estimated module 

temperature.

Figure 3. Basic description of the steps needed to create and verify a performance guarantee for 

a new PV system. Details vary, but, today, the vast majority of contracts use broadband 

irradiance data as the heart of the performance prediction.

One major problem with this method is that errors are introduced when relating broadband 

historical irradiance data to PV performance due to spectral errors, angle of incidence errors, etc. 

However, this problem would be resolved if historical data were available for PV-matched 

irradiance data. Alternatively, if a model is created that translates the historical broadband data 

into standardized PV-matched data sets, the models used for the original and updated predictions 

could be aligned in an unambiguous way as shown in Figure 4. The colors of the boxes for the 

models shown in Figures 3 and 4 are intended to indicate the level of uncertainty (red for higher 

uncertainty and green for lower uncertainty), but the uncertainties are much more complicated 

than can be captured with this simple color scheme.
7 

Figure 4. Method for creating and verifying performance guarantee if PV-matched irradiance data 

could be made available.

As an example of the results of such an implementation, we show in Figure 5 the results of a 

calculation of the difference between clear-sky daily irradiance as measured with a c-Si PV 

reference cell and a thermopile pyranometer in Houston, Texas, and Phoenix, Arizona, calculated 

using TMY data and a solar spectrum simulation program [8]. From Figure 5 it is evident that 

errors in daily insolation between a thermopile pyranometer and a c-Si PV reference cell due 

solely to spectral effects can be as large as 3%, and that this error is highly variable over days, 

weeks, and months. We emphasize that these results include spectral effects only: angle of 

incidence and other effects that may further exacerbate the errors between broadband irradiances 

measured with pyranometers and actual usable fuel available to PV power plants have not been 

included. 
8 

Figure 5. Example calculated differences (using clear-sky spectral modeling) from TMY data 

between daily insolation as measured with a thermopile pyranometer and a c-Si PV reference cell. 

Differences shown are due solely to spectral effects. 

Broadband Irradiance Measurement Uncertainties

Even in the case that the goal of the user is to measure broadband irradiance, a PV reference cell 

may exhibit the same or better (under other conditions) measurement uncertainties than 

thermopile pyranometers. We calculated broadband irradiance uncertainties using the same 

method for calculating irradiance measurement uncertainties described in detail in Reference [2], 

but assigning the spectral error to the PV reference cell instead of the thermopile pyranometer. 

The results, summarized in Table 3, show typical irradiance measurement uncertainties for both 

broadband and spectrally matched irradiance measurements. These measurement uncertainties 

correspond to clear sky conditions at 1000 W/m2, and confidence intervals of 95%. We 

emphasize that lower-class pyranometers, rapidly varying irradiance, or deviations from clear 

sky atmospheric conditions will result in higher thermopile pyranometer measurement 

uncertainties.
9 

Table 3. Combined expanded uncertainties associated with typical PV reference cell and 

thermopile pyranometer measurements of broadband irradiance, as well as spectrally corrected 

irradiance (i.e., useable fuel for PV systems). The uncertainties shown here correspond to clear 

sky conditions at 1000 W/m2

.

1

Resource data Thermopile 

Pyranometer

Matched reference cell

Broadband ~4.3% ~4.3%

PV “useable” fuel ~5.3% ~2.4%

When is a Reference Cell Most Advantageous?

Use of a PV reference cell: 

1. Improves the accuracy of performance measurements, which should reduce the cost of 

capital and LCOE. This requires that the initial performance projections be performed 

with appropriate resource data, which can be accomplished by modeling the spectrum, as 

is shown in Figure 5, or collecting resource data with a reference cell during site 

assessment.

2. Enables precise measurement of PV performance to detect degradation or deviation from 

expected performance. 

Currently most long-term resource datasets contain only broadband irradiance data and, 

therefore, most resource assessments and initial performance projects are conducted using 

broadband irradiance data. When the initial performance projections are modeled with 

broadband irradiance data the uncertainties in predicted PV power generation will be larger than 

if the portion of the solar resource available to PV modules were measured using a spectrally 

matched reference cell. Thermopile pyranometers exhibit increased uncertainty relative to solar 

reference cells and do not respond to changes in environmental conditions in exactly the same 

way as PV modules. Use of pyranometers may still be useful for comparing observed 

broadband irradiance to historic broadband irradiance, but the adoption of PV performance 

projections made using PV-matched irradiance data2 coupled with measured performance using 

PV reference cells should 1) reduce uncertainty in performance, 2) reduce the working cost of 

capital to the plant owner, and, therefore, 3) decrease the LCOE of electricity from PV power

plants. 

 

1 One subtle issue here is the angular response of the PV reference cell and its effect on the measurement of the 

diffuse component of global radiation. We assume here that this source of uncertainty is minimized by the use of a 

PV reference cell that has been calibrated outdoors under clear-sky conditions, thus incorporating the diffuse-light 

contribution to measured irradiance during its calibration. This effect is automatically corrected when using a 

matched PV reference cell to measure “useable fuel” for PV modules. Interested readers are referred to Reference 

[9] for a more detailed discussion.

2 Irradiance data is usually documented by its direct and diffuse components, facilitating translation to surfaces of 

arbitrary orientation. Currently, commercially available instruments do not facilitate measurement of direct and 

diffuse irradiance with matched reference cells.
10 

Acknowledgements

This work was supported by the U.S. Department of Energy under Contract No. DE-AC36-

08GO28308 with the National Renewable Energy Laboratory. The Alliance for Sustainable 

Energy, LLC (Alliance), is the manager and operator of the National Renewable Energy 

Laboratory (NREL). Employees of the Alliance, under Contract No. DE-AC36-08GO28308 with 

the U.S. Dept. of Energy, have authored this work. The United States Government retains and 

the publisher, by accepting the article for publication, acknowledges that the United States 

Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or 

reproduce the published form of this work, or allow others to do so, for United States 

Government purposes.

References

[1] ANSI C12.20, “Electricity Meters - 0.2 and 0.5 Accuracy Classes,” American National 

Standards Institute. 

[2] “Comparison of Pyranometers vs. PV Reference Cells for Evaluation of PV Array 

Performance,” by L. Dunn, M. Gostein, and K. Emery. Presented at the 38th IEEE Photovoltaic 

Specialists Conference, Austin, TX, 2012. 

[3] “Improved Test Method to Verify the Power Rating of a Photovoltaic (PV) Project,” by A. 

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[4] IEC 60904-3:2008: Photovoltaic devices - Part 3: Measurement principles for terrestrial 

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Electrotechnical Commission, Geneva, Switzerland, 2008. 

[5] www.pvsyst.com

[6] https://sam.nrel.gov 

[7] “Method to Calculate Uncertainties in Measuring Shortwave Solar Irradiance Using 

Thermopile and Semiconductor Solar Radiometers,” by Ibrahim Reda. Technical Report 

Number NREL/TP-3B10-52194. National Renewable Energy Laboratory, 2011.

[8] http://rredc.nrel.gov/solar/models/spectral/ 

[9] “Effect of the Angle Dependence of Solar Cells on the Results of Indoor and Outdoor 

Calibrations,” by S. Winter, D. Friedrich, T. Gerloff. Presented at the 25th European 

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