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

Evaluation of Photodiode and 

Thermopile Pyranometers for 

Photovoltaic Applications

Preprint

M. Sengupta, P. Gotseff, and T. Stoffel

To be presented at the 27th European Photovoltaic Solar Energy 

Conference and Exhibition

Frankfurt, Germany

September 24–28, 2012

Conference Paper

NREL/CP-5500-56540

September 2012
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1

EVALUATION OF PHOTODIODE AND THERMOPILE PYRANOMETERS FOR PHOTOVOLTAIC 

APPLICATIONS

Manajit Sengupta, Peter Gotseff, and Thomas Stoffel

National Renewable Energy Laboratory

15013 Denver West Parkway, Golden, CO 80401-3305, USA

ABSTRACT: Accurately determining photovoltaic (PV) module performance in the field requires measuring solar irradiance

reaching the PV panel at a high level of accuracy and known uncertainty. Silicon detectors used in various solar energy 

measuring instruments that include reference cells are a potentially attractive choice for multiple reasons, including faster

responsivity than thermopile detectors, cheaper cost, and lower maintenance. The main drawback, however, is that the silicon

detectors are only spectrally responsive in a narrow part of the solar spectrum; therefore, to determine broadband solar

irradiance, a calibration factor that converts the narrowband response to broadband is required. Normally, this calibration factor

is a single number determined under standard conditions but used for various scenarios, including varying airmass, panel

orientation, and atmospheric conditions. This would not have been an issue if all wavelengths that form the broadband spectrum

responded uniformly to atmospheric constituents. Unfortunately, the scattering and absorption signature varies widely across

wavelengths, and the calibration factor computed under certain test conditions is not appropriate for other conditions. This

paper lays out the issues that will arise from the use of silicon detectors for PV performance measurement in the field. We

also present a comparison of simultaneous spectral and broadband measurements from silicon and thermopile detectors and

estimated measurement errors when using silicon devices for both array performance and resource assessment.

Keywords: spectral response, photodiodes, pyranometer, direct normal irradiance, global horizontal irradiance

1 INTRODUCTION

Silicon photodiode-based pyranometers have been used to

measure global horizontal irradiance (GHI), primarily in

agricultural networks, for decades. These radiometers are also

popular for applications in solar energy conversion. They are

currently used in numerous locations to measure GHI for

various purposes, including solar resource assessment and

photovoltaic (PV) performance [1]. PV performance testing

requires accurate measurements of both power output by PV 

panels and solar energy incident on the panels (plane-of- array,

or POA, irradiance). These silicon devices have become popular

mainly because of their low cost, ease of maintenance, and fast

time response for high frequency data. Silicon photodiode 

pyranometers provide limited spectral response, as shown in Fig.

1. The direct normal irradiance (DNI) spectral irradiance shown

in Fig. 1 illustrates the magnitude of this limitation. The 

calibration of pyranometers is based on simultaneous 

measurements of solar irradiance by a broadband thermopile 

reference (REF) and the unit under test (UUT) [2]. The

resulting pyranometer responsivity is computed as the ratio

UUT (μV) / REF (Wm-2

). For the photodiode- based

pyranometer, this calculation represents the energy collected by 

a silicon device to the total energy available in the solar

spectrum. Calibration data are generally collected throughout

the day under clear-sky conditions. Unfortunately, the solar

spectrum does not change uniformly with increasing airmass;

therefore, the ratio of the energy gathered by a silicon

photodiode pyranometer compared with the total energy in the

solar spectrum will vary during the day. This paper seeks to

understand the impact of this variability and whether the use of

a constant calibration coefficient results in significant error in

estimation of broadband POA irradiance using a silicon

photodiode pyranometer.

Fig. 1. Spectral response function of a silicon photodiode 

pyranometer (in green) shown along with a spectral DNI 

measurement from a summer and winter day (shown in red and 

blue, respectively).

2 MOTIVATION

We designed a single-axis tracking device at the National 

Renewable Energy Laboratory (NREL) and mounted multiple

silicon devices, including a LICOR model LI-200, an Apogee

model SP 110, an IMT reference cell, as well as a Kipp and

Zonen model CMP 11 thermopile pyranometer. The goal of this

instrument package was to investigate the possibility of

deploying such single-axis tracking devices in the field for solar

resource assessment relevant to a similarly tracking PV plant

(Fig. 2). We observed that the silicon-based devices had

significant measurement difference when compared to each

other based on the manufacturer’s calibration. We also observed
2

that although the LI-200 measurements agreed with the CM 11

at solar noon, over-prediction of GHI occurred earlier in the

morning and later in the afternoon. (Fig. 2). The CM 11

thermopile device was validated against a reference CM 21 at

solar noon.

Fig. 2. Silicon and thermopile device measurement mounted on

a single-axis tracker. GHI measurements from a well-calibrated

instrument are also shown.

The silicon devices were scaled to the LI-200 using the 

solar noon offset as the correction, as shown in Fig. 3. It was

clear that all silicon devices agreed with the CM 11

thermopile instrument at solar noon but over-predicted both in

the morning and afternoon.

Fig. 3. Silicon and thermopile device measurement mounted on

a single-axis tracker. The measurements from the silicon devices

were scaled to the LI-200. GHI measurements from a wellcalibrated instrument are also shown.

The difference between the silicon and thermopile devices was 

significant enough that over-prediction similar to that observed

in this case will result in significant errors in PV performance

evaluation if silicon devices are used. In this paper, we 

investigate the impact of spectral sensitivity of silicon devices

and whether a static calibration of the silicon devices leads to

errors in measurement when compared to broadband 

measurements by well-calibrated thermopile devices.

3 METHODOLOGY AND RESULTS

The spectral distributions of measured DNI and GHI 

changed as the solar zenith angle changes during the course of

the day. Fig. 4 shows how the spectral DNI changed during the

course of a clear day. Fig. 5 shows how the spectral GHI 

changed during the same day.

Fig. 4. Hourly DNI spectra for April 6, 2012, measured from 6

a.m. to 12 p.m. The dashed black line represents the spectral

response of the LI-200 instrument. The dotted black line

represents the spectral response of the diffuser on the LI-200.

Fig. 5. Hourly GHI spectra for April 6, 2012, measured from 

6 a.m. to 12 p.m. The dashed black line represents the spectral

response of the LI-200 instrument. The dotted black line

represents the spectral response of the diffuser on the LI-200.

The spectral DNI was measured using a Kipp and Zonen 

PGS 100 spectrophotometer with the spectra being measured

from 350 nanometers (nm) to 1050 nm at a resolution of

approximately 4 nm. A LI-COR LI-1800 spectroradiometer

measures spectral GHI between the wavelengths of 350 nm and

1100 nm.

The DNI spectrum for various times showed that the 

distribution changed during the day as the airmass changed (see

Fig. 4). As shown in Fig. 5, similar changes occurred in the GHI 

spectrum. The LI-200 instrument receives a signal from each of

the solar wavelengths scaled to the product of the response of

the photodiode and the diffuser. The total signal received is

the sum of energy received from each of the wavelengths. To 

measure broadband solar radiation with the LI-200 under 

current practice, the instrument is calibrated to an airmass of 

1.5. This static calibration can produce accurate broadband

measurements at other times of the day only if the spectral 

distribution has the same shape as at calibration time, implying

that the proportion of energy in various wavelengths remains the 

same. As in a Rayleigh scattering environment, shorter

wavelengths are preferentially scattered the shape of the spectral 

distribution, especially in the DNI, which varies (Fig. 4); 

therefore, we expect that a static calibration for the LI-200

for an airmass of 1.5 will lead to a biased measurement at other

times of the day with different airmass.

To investigate the impact of spectral shape changes on LI200 measurement errors, we took the spectral DNI 

measurements from the PGS-100 instrument and convolved it

with the sensor and diffuser spectral responses for a zenith angle

of 45 degrees, corresponding approximately to an airmass of

1.5. We then summed the total energy in the convolved

calculation for both DNI and GHI to arrive at an estimate of 

“actual energy” received at the sensor. To create a “calibration” 
3

for measuring broadband DNI, we took the broadband 

measurement from the Kipp and Zonen CH-1 model 

pyrheliometer (a thermopile instrument) for exactly the same

time and location and calculated a ratio of the broadband

measurement to the “actual energy” from the PGS-100. We 

called this the “calibration coefficient,” which was then applied

to the convolved spectral sum from the PGS-100 at other times

to obtain the broadband solar radiation. A similar method was

applied to the spectral GHI measured using the LI-1800. The 

broadband measurement from the Kipp and Zonen CM-22, a 

thermopile instrument, was used to compute a similar 

“calibration coefficient” at airmass 1.5 for silicon devices.

Next, we applied the “calibration coefficients” for 

converting spectral DNI and GHI to the convolved spectral sum

for measurements taken at various times on a clear day and

compared them to the CH-1 measurements for DNI and CM-22

for GHI. We also took the DNI and GHI measurements from the

rotating shadowband radiometer (RSR), which had a LI-200

for measurement, and calculated the differences between the 

RSR and thermopile instruments. In this experiment, if the 

differences between the RSR and thermopile instruments were

similar to the difference observed in the spectral instrument

versus thermopile comparison, we would be able to say 

definitively that spectral mismatch results in errors in broadband

measurements when silicon based instruments are used.

4 RESULTS

June 22, 2011, was observed to be a clear day at NREL’s

Solar Radiation Research Laboratory in Golden, Colorado, as

shown in Fig. 6. The results for the DNI comparison are shown

in Fig. 7.

The red line in Fig. 7 is our estimate of errors from using a

silicon instrument such as the LI-200 because of the change in

the energy distribution of the observed spectra. The shape of

our estimated errors matched the actual errors from comparing

RSR and CH-1 measurements at the same location for the same 

day.

Fig. 6. The observed DNI from a CH-1 (red) and GHI from a

CM-22 (green) for June 22, 2011, shows a clear day.

Fig. 7. Difference in broadband DNI calculated using the 

spectral DNI and measurements from the CH-1 thermopile

instrument are shown by a red line as a function of zenith

angle. Note that there was no difference between the two at a

zenith angle of 45 degrees, the calibration point. The dashed

black line shows the observed differences between the

measurement using the LI-200 and the CH-1 for the same day.

The green line shows the differences if the spectral response 

of the diffuser was excluded from the spectral “calibration” and

calculation. Data used were from June 22, 2011.

The errors for high zenith angles were significant and can

reach 50 W/m^2. It is notable that the morning and afternoon

errors were slightly different, both in our estimates (red) and

actual measurements (black). This difference in the errors is

attributed to a difference in aerosol loading in the atmosphere,

where the spectral distribution is again impacted because of

scattering by the aerosol. A closer look at Fig. 4 and Fig. 7

clearly shows that the error grows as the spectral shape changes.

Fig. 8. Difference in broadband GHI (calculated using the 

spectral DNI difference plus the diffuse difference between the

RSR diffuse and thermopile diffuse) is shown by the blue line as

a function of zenith angle. The dashed black line shows the

observed differences between the measurement using the LI200 and the CM-22 for the same day. Data used were from June 

22, 2011.

In Fig. 8, the blue line shows our theoretical estimate of GHI

errors. To calculate the GHI errors, we first calculated diffuse

errors using the diffuse measurements from the RSR and the 

diffuse from a shaded CM-22. We then scaled the estimated

DNI difference from Fig. 7, scaled it by the cosine of the solar

zenith, and added the diffuse difference to calculate the GHI

error estimates. The dotted black lines show the actual errors. It
4

is interesting to note that the errors in GHI were not as high as

observed for DNI. This observation was supported by the fact

that the spectral GHI in Fig. 5 did not change shape as

drastically as the spectral DNI in Fig. 4. This phenomenon can

be explained by the fact that the blue light at shorter

wavelengths that has been preferentially scattered out of the 

direct beam forms part of the diffuse radiation that reaches the

surface as a component of the GHI.

Nevertheless, we found that both GHI and DNI

measurements using silicon instruments have errors that are

dependent on zenith angle. Other influences were the aerosol

loading, as shown in both Fig. 7 and Fig. 8, where the morning 

and afternoon errors varied because of a change in aerosol

loading.

5 SUMMARY

We found that broadband measurements using silicon 

devices deviate from measurements using a thermopile. All

measurements were taken using a single-axis tracking platform.

As silicon devices have a variable response across the solar

spectrum, they are calibrated to broadband thermopile devices at

solar zenith angles below 45 degrees, in accordance with

protocol. The solar DNI spectrum does not vary uniformly with

airmass, because blue light is preferentially scattered out with an 

increase in airmass; therefore, the calibration coefficient 

calculated at a particular zenith angle is no longer valid at higher

solar zenith angles. This results in over-prediction of broadband

solar radiation at higher zenith angles and under-prediction at

lower zenith angles. This error must be corrected when

determining the absolute efficiency of PV devices. It is expected

that similar errors will occur if the calibration coefficient is

calculated for a particular environmental condition and the 

silicon device is deployed in a different environment. As an

example, higher aerosol loading will cause similar preferential

scattering in the blue part of the solar spectrum and cause 

similar over-prediction. Additionally, calibrations conducted at

higher elevations and low water vapor conditions will no

longer be applicable at lower elevations and humid

conditions; therefore, we concluded that the use of silicon

devices for PV performance evaluation will lead to uncertainties 

that cannot easily be quantified. Empirical correction factors

have been devised to correct for the spectral errors[4]. Such 

methods may not be able to provide accurate corrections for

diverse conditions seen at various locations.

6 ACKNOWLEDGEMENT

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

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

Renewable Energy Laboratory.

7 REFERENCES

[1] Stoffel, T.L., et al. Concentrating Solar Power: Best

Practices Handbook for the Collection and Use of Solar

Resource Data. NREL/TP-550-47465. Golden, CO: National 

Renewable Energy Laboratory, 2010; 146 pp. Accessed May 9,

2012. www.nrel.gov/docs/fy10osti/47465.pdf.

[2] Myers, D.; Stoffel, T.L.; Andreas, A.; Wilcox, S.; Reda, I.

Improved Radiometric Calibrations and Measurements for

Evaluating Photovoltaic Devices. NREL/TP-520-28941.

Golden, CO: National Renewable Energy Laboratory, 2000; 43

pp. Accessed May 9, 2012: http://gisceu.net/PDF/U772.pdf.

[3] Solar Radiation Research Laboratory Baseline Measurement

System. Accessed May 9, 2012: www.nrel.gov/midc/srrl_bms.

[4] King, D.L.; Boyson, W.E.; Hansen, B.R.; Bower, W.I.

“Improved Accuracy for Low-Cost Solar Irradiance Sensors.”

2nd World Conference and Exhibition on Photovoltaic Solar

Energy Conversion Proceedings; July 6–10, 1998, Vienna,

Austria.