Evolution of NREL's National Solar Radiation Database (NSRDB)
This poster provides an overview of the National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB).
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Engineering topics
Sengupta, Manajit
Publications and source records attributed to Sengupta, Manajit.
ASTM Solar Resource Standards for Solar Energy Industry
This poster provides an overview of of the ASTM solar resource standards for the solar energy industry.
14 SOLAR ENERGY↗
Adjoint Sensitivity of FARMS to the Forecasting Variables of WRF-Solar: Preprint
This study presents the development and application of an adjoint model for investigating the sensitivity of solar radiation to forecasted variables from WRF-Solar. The first part of this study focuses on developing an adjoint model for the Fast All-sky Model for Solar Applications (FARMS) to investigate the input variables having the highest sensitivity to global horizontal irradiance (GHI), direct normal irradiance (DNI), and diffuse horizontal irradiance (DHI), which are the output variables. The applicability and usefulness of the adjoint sensitivity approach are demonstrated by conducting a sensitivity analysis under various scenarios defined by low, medium, and high values for the input variables. This preliminary study uses elasticity values to understand the sensitivity of solar radiation to the input variables (e.g. solar zenith angle, Angstrom turbidity coefficient, and cloud optical depth) of FARMS. This presentation will illustrate the implemented methodology and the obtained sensitivity results for FARMS, as well as future research steps that will lead to the development of high-quality probabilistic solar forecasts.
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Adjoint Sensitivity Analysis of FARMS for Forecasting Variables of WRF-Solar
This poster provides an overview of adjoint sensitivity analysis of FARMS for forecasting variables of WRF-solar.
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Data Quality Assessment Using SERI-QC
This presentation provides an overview of data quality assessment using SERI QC.
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The National Solar Radiation Database (NSRDB) for CSP Applications
This study examines the differences in direct normal irradiance (DNI) in two versions of the National Solar Radiation Database (NSRDB) produced by the National Renewable Energy Laboratory (NREL). NSRDB V3 of the NSRDB includes significant changes to various parts of the radiative transfer model and inputs to the model compared to NSRDB V2. The changes in NSRDB V3 resulted in lower uncertainty than NSRDB V2. The study quantified the uncertainty and the spatial and temporal variability under clear-sky conditions. The uncertainty estimation was performed using a standardized method, The Guide to the Expression of Uncertainty in Measurement (GUM). The evaluation of the accuracy of the NSRDB was conducted using high-quality ground-measurements for seven National Oceanic and Atmospheric Administration Surface Radiation Budget Network (SURFRAD) stations for 1998-2015.
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Removing Biases from Rotating Shadowband Radiometers
Three types of biases are examined for a Rotating Shadowband Radiometer (RSR): temperature bias, spectral bias, and deviation from a Lambertian cosine response. A step by step method is presented to illustrate how to use this information to develop a model for adjustment algorithms for a RSR. Comparisons are made with a RSR adjusted using the model and measure direct normal, diffuse, and global irradiance.
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SOLARUN (Solar Resource Uncertainty Application) [SWR-19-41]
A critical component of designing solar energy projects that will have long-term profitability is the availability and access to accurate “bankable data”, methodologies and tools to understand the uncertainties in the data. The current solar energy performance and economic models used to convert solar resource data to power generation do not factor in or provide adequate information regarding the uncertainty in the solar resource information. The Solar Resource Uncertainty (SOLARUN) Application assists stakeholders to quantify solar resource measurement uncertainty from radiometers which ultimately support in the decision-making process, speeds up the acceptance and deployment of projects and reduces risk to the financiers and developers.
Habte, Aron↗
Ventilator Improvements for Reducing Radiometer Frost, Snow, and Ice Accumulation
This poster provides an overview of ventilator improvements for reducing radiometer frost, snow, and ice accumulation.
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Data Quality Assessment Using SERI-QC
This poster provides an overview of data quality assessment using SERI QC.
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The Case for Custom TMYs: Examples Using the NSRDB
This presentation provides an overview of custom typical meteorological year data sets using the National Solar Radiation Database.
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Spectral and Broadband Data Sets from the National Solar Radiation Database (NSRDB)
This presentation provides an overview of spectral and broadband data sets from The National Solar Radiation Database (NSRDB).
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Improved Field Evaluation of Reference Cell Using Spectral Measurements: Preprint
Spectral irradiance measurements as opposed to modeled spectral values are used to evaluate the effects of changing spectral distributions on the output of reference solar cells. Comparisons are made on a one-axis tracking surface to a Class A pyranometer. The ratio of the reference cells to reference pyranometers exhibit a bias over the day. Spectral and temperature modeling reproduce most but not all changes in the ratio over the day. Sources for this difference in modeling and performance of the reference cells are examined.
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The Case for Custom TMYs: Examples Using the NSRDB: Preprint
A typical meteorological year (TMY) essentially represents an hourly compilation of median months constructed using multiyear data sets. Although TMYs are generated from global horizontal irradiance, they are used in photovoltaic (PV) modeling for various plane-of-array (POA) representations. This paper demonstrates that a POA TMY generated with corresponding POA irradiance time-series information produces significantly different results than a POA TMY generated by transposing a TMY data set. In some months, the differences can be more than 3%. These results point to the need for generating TMYs using POA irradiance time series representing the orientation at which PV panels will be deployed.
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A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers
Not Available
Gueymard, Christian A.↗
A Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT): Part II. The cloudy-sky model
The Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT) reported in Part I of this report is enhanced to include the requirements for cloudy-sky conditions. Surface radiances in 2002 narrow-wavelength bands from 0.28 to 4.0 um are analytically computed by solving the radiative transfer equation for five independent photon paths accounting for clear-sky absorption, Rayleigh scattering, and cloud absorption and scattering. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) is used to show the optical thickness of the clear-sky atmosphere. Unlike Part I, which approximates the computation of aerosol scattering using the single-scattering phase function, the cloud transmittance and reflectance are efficiently retrieved from a comprehensive look-up table pre-computed by a 32-stream DIScrete Ordinates Radiative Transfer (DISORT) model for possible cloud conditions as well as solar and viewing geometries. A resolution assessment is performed to observe the optimal balance between the computational efficiency and accuracy in the development of the look-up table. Model simulations by DISORT and TMYSPEC are used to evaluate the performance of FARMS-NIT under cloudy-sky conditions. Compared to DISORT, FARMS-NIT yields 2-3% uncertainties on average, but it substantially reduces the computational time because of the independent computation of cloud properties and the implementation of the look-up table. In contrast to TMYSPEC, which uses successive steps to empirically compute plane-of-array (POA) irradiances and spectral irradiances, FARMS-NIT directly solves spectral radiances from the radiative transfer equation, which profoundly increases the accuracy in surface irradiances, especially over inclined photovoltaics (PV) panels.
14 SOLAR ENERGY↗
A Fast All-Sky Radiation Model for Solar Applications with Narrowband Irradiances on Tilted Surfaces (FARMS-NIT): Part II. The Cloudy-Sky Model
The Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMSNIT) reported in Part I of this study is enhanced to include the requirements for cloudy-sky conditions. Surface radiances in 2002 narrow-wavelength bands from 0.28 to 4.0 μm are analytically computed by solving the radiative transfer equation for five independent photon paths accounting for clear-sky absorption, Rayleigh scattering, and cloud absorption and scattering. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) is used to provide the optical thickness of the clear-sky atmosphere. Unlike Part I, which approximates the computation of aerosol scattering using the single-scattering phase function, the cloud transmittance and reflectance are efficiently retrieved from a comprehensive look-up table pre-computed by a 32-stream DIScrete Ordinates Radiative Transfer (DISORT) model for possible cloud conditions as well as solar and viewing geometries. A resolution analysis is performed to assess the optimal balance between the computational efficiency and accuracy in the development of the look-up table. Model simulations by DISORT and TMYSPEC are used to evaluate the performance of FARMS-NIT under cloudy-sky conditions. Compared to DISORT, FARMS-NIT yields 2–3% uncertainties on average, but it substantially reduces the computational time because of the independent computation of cloud properties and the implementation of the look-up table. In contrast to TMYSPEC, which uses successive steps to empirically compute plane-of-array (POA) irradiances and spectral irradiances, FARMS-NIT directly solves spectral radiances from the radiative transfer equation, which profoundly increases the accuracy in surface irradiances, especially over inclined photovoltaics (PV) panels.
Xie, Yu↗
Improving the Accuracy of the National Solar Radiation Database (1998-2016): Preprint
The National Solar Radiation Database (NSRDB) provides gridded satellite-based data sets for the United States and other parts of North and South America using cloud properties retrieved from the Geostationary Operational Environmental (GOES) series of satellites. These cloud properties in combination with ancillary atmospheric parameters are used in the Physical Solar Model (PSM) to compute solar radiation. This paper seeks to 1) describe the changes made in the PSM for the recent update to the National Solar Radiation Database (NSRDB 1998-2016) and 2) examine the performance of this update by comparing it to high-quality ground-based measurements. We summarize the changes to the input data for the radiative transfer models as well as updates for the processing algorithms. We also analyze the performance and accuracy of the NSRDB across the United States. Additional details of the data set and model are described in (1), (2), (3), and (4).
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