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Contract No. DE-AC36-08GO28308

Conference Paper 

NREL/CP-5D00-77790 

September 2020 

Evaluation of the PV Resource Dataset

in Central and North America

Preprint

Yu Xie and Manajit Sengupta 

National Renewable Energy Laboratory

Presented at the 37th European Photovoltaic Solar Energy Conference and 

Exhibition (EU PVSEC 2020)

September 7–11, 2020 
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Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

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

Contract No. DE-AC36-08GO28308

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Conference Paper 

NREL/CP-5D00-77790 

September 2020 

Evaluation of the PV Resource Dataset 

in Central and North America

Preprint

Yu Xie and Manajit Sengupta 

National Renewable Energy Laboratory 

Suggested Citation 

Xie, Yu, and Manajit Sengupta. 2020. Evaluation of the PV Resource Dataset in Central 

and North America: Preprint. Golden, CO: National Renewable Energy Laboratory. 

NREL/CP-5D00-77790. https://www.nrel.gov/docs/fy21osti/77790.pdf. 
NOTICE

This work was authored 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 herein do not necessarily represent the views of the DOE or the U.S. 

Government.

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This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

EVALUATION OF THE PV RESOURCE DATASET IN CENTRAL AND NORTH AMERICA

Yu Xie and Manajit Sengupta

National Renewable Energy Laboratory

15013 Denver W Pkwy, Golden, CO 80401

ABSTRACT: A recent effort from the National Renewable Energy Laboratory (NREL) has led to a new radiative 

transfer model, Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces 

(FARMS-NIT), to efficiently compute plane-of-array (POA) irradiance. This model has been implemented by the 

National Solar Radiation Data Base (NSRDB) to provide photovoltaic (PV) resource over Central and North America 

in both narrowband and broadband wavelengths. This study conducts a comprehensive evaluation of the PV resource 

dataset using surface-based observations in various climate zones. The results demonstrate that the PV resource has an 

excellent agreement with the cloudy-sky observations from thermopiles and reference cells.

Keywords: solar radiation, POA irradiance, PV Resource

1 INTRODUCTION 

Models estimating the production from solar energy 

conversion systems often assume isotropic diffuse 

radiation in both the downwelling and upwelling 

directions [1-3]. Other conventional models compute 

plane-of-array (POA) irradiances by empirically

converting global horizonal irradiance (GHI) and direct 

normal irradiance (DNI) to irradiance over inclined

surfaces [4]. Xie and Sengupta [1] developed the Fast Allsky Radiation Model for Solar applications with 

Narrowband Irradiances on Tilted surfaces (FARMS-NIT) 

to solve the radiative transfer equation for the individual

photon paths and thereby compute spectral irradiance in 

the POA [5, 6]. The clear-sky atmospheric properties, e.g. 

the optical thickness of the atmosphere, are efficiently 

solved by the Simple Model of the Atmospheric Radiative 

Transfer of Sunshine (SMARTS) [7] in 2,002 wavelength 

bands from 0.28-4.0 μm. The clear-sky radiances are 

numerical given by computing the absorption, scattering 

and reflection by aerosols with a single-scattering 

assumption. The cloudy-sky radiances are associated with 

the cloud transmittance pre-computed by a 64-stream 

discrete ordinates radiative transfer (DISORT) model [8] 

and Rayleigh scattering above and under the cloud. The 

integration of the radiances over the inclined surfaces 

leads to spectral POA irradiances for any PV tilt 

directions. Following the advanced studies on the 

atmospheric properties using satellite observations [9-18], 

the FARMS-NIT has been implemented by the National 

Solar Radiation Data Base (NSRDB) [19, 20] to provide 

photovoltaic (PV) resource over Central and North 

America. Although the model has been validated using 

surface-based observations at the National Renewable 

Energy Laboratory’s (NREL’s) Solar Radiation Research 

Laboratory (SRRL), more comprehensive validation is 

needed in various scenes and climate zones. In this study, 

the PV resource data disseminated by the NSRDB website 

are validated by the observations from thermopiles and 

reference cells provided by the First Solar, Inc.

2 THE PV RESOURCE DATA

Figure 1 demonstrates the pop-up window in the NSRDB 

website to download PV resource data. With users’ 

selection of location and PV orientation, FARMS-NIT and 

NSRDB data are used to compute the POA irradiance in 

real time. The data for each round hour will be freely 

available with an email notification within 1–10 minutes 

after receiving the users’ order. More details on the PV 

Resource data is available from the NSRDB website, 

https://nsrdb.nrel.gov. 

Fig.1 The pop-up window in the NSRDB website to 

download PV resource data.

3 THE EVALUATION OF THE PV RESOURCE 

DATA

3.1 Data from the First Solar, Inc.

Fig. 2 The surface sites operated by the First Solar, Inc.

Figure 2 demonstrates the surface sites operated by the 

First Solar, Inc., which are located in (1) Picture Rocks, 

AZ; (2) Neenach, CA; (3) Deming, NM; (4) Calipatria, 

CA; (5) Sarnia, Canada; and (6) London, Canada. The 

green, red, and blue dots represent observations from 

fixed-tilt surfaces, 1-axis trackers, and both, respectively. 

Long-term data, roughly from 2013 to 2018, with a 
2 

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temporal resolution of 1 minute, are used to validate the 

PV Resource data.

3.2 Evaluation of the PV Resource data

Fig. 3 Comparisons of GHIs and POA irradiances between 

surface observations and PV Resource data in Picture 

Rocks, AZ.

Fig.4 Comparisons of GHIs and POA irradiances between 

surface observations and PV Resource data in Neenach, 

CA.

The evaluation of the PV resource data is performed using 

the long-term observations from 2013–2018. Figure 3 

displays the comparisons of GHIs and POA irradiances 

between surface observations and PV Resource data in

Picture Rocks, AZ. The black lines denote the PV 

Resource data. The solid and dashed color lines represent 

observations from thermopiles and reference cells, 

respectively. The upper and lower rows represent GHI and 

POA irradiance on a single-axis tracker, respectively. As 

shown, the PV Resource data have an excellent agreement 

with the observations in the clear-sky conditions. The 

mismatch in the cloudy-sky POA irradiance is linked with 

the uncertainty in the cloud product of the Geostationary 

Operational Environmental Satellite (GOES) because 

similar uncertainty is also found in the GHI computation.

Similar findings are shown in Fig.4, where the POA 

irradiances on fixed-tilt surfaces are compared with the PV 

Resource data. Also shown is that the measurements of the 

POA irradiance from different instruments are more biased

than the GHI measurements. 

Fig.5 Comparisons of cloudy-sky GHI between surface 

observations and PV Resource data.

Figure 5 compares cloudy-sky GHIs for the six sites 

between surface observations and PV Resource data. The 

cloudy-sky GHIs are slightly underestimated by the PV 

Resource data when the clouds are thick. When the clouds 

are thin, the GHIs are slightly overestimated. This is 

probably because high-resolution surface observations are 

occasionally associated with clear-sky scenes in the 1-hour 

durations. This effect becomes less visible when the clouds 

are thin.
3 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. 

Fig. 6 Comparisons of cloudy-sky POA irradiances 

between PV Resource data and surface observations from 

fixed-tilt surfaces.

Figure 6 shows the comparisons of cloudy-sky POA 

irradiances between PV Resource data and surface 

observations from fixed-tilt surfaces at three sites: 

Neenarch CA; Sarnia, Canada; and London, Canada. It 

shows that the PV Resource data also have a decent 

performance on the POA irradiances over inclined 

surfaces. Future efforts on satellite remote sensing and 

cloud retrieval techniques are required to further reduce 

the uncertainty of the data.

3 CONCLUSIONS 

This study evaluates the PV Resource data from the 

NSRDB using surface observations from six sites in the 

Central and North America that are operated by the First 

Solar, Inc. The 1-min resolution surface data are averaged 

over each hour and compared with the nearest pixel of the 

PV Resource data. Our results demonstrate that the PV 

Resource data have an excellent agreement with the 

cloudy-sky observations from thermopiles and reference 

cells. Slight underestimations are observed in thick-clouds 

conditions. This effect is moderated when the clouds are 

thin. This indicates that satellite observations with better 

temporal resolutions can effectively reduce the current 

uncertainty in the PV Resource data. Further, more 

analysis is needed—particularly under clear-sky 

conditions and scattered-cloud scenes—to 

comprehensively understand the performance of the PV 

Resource data.

ACKNOWLEDGMENTS 

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. This work 

was supported by the U.S. DOE Office of Energy 

Efficiency (EERE) Solar Energy Technologies Office 

(SETO). 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. We 

acknowledge the DOE Solar Energy Technologies 

Program and its PV subprogram for supporting this 

research. Specifically, we thank Dr. Dave RenchMcCauley, Dr. Lenny Tinker, Dr. Rebecca JonesAlbertus, and the PV subprogram for their support and 

encouragement.

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