In this document
We will explain why solar irradiance datasets are one the most critical inputs for power plant design and simulation, significantly impacting the reliability of expected power output. Their accurate estimation and inclusion are essential for reliable energy yield predictions, as they determine the amount of solar energy available to PV systems.
Overview
Solar irradiance is the primary determinant of the energy available to PV systems, with Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) serving as its key components. Both play a critical role in photovoltaic (PV) simulations for the calculation of total incident energy on the power plant and simulation of system performance.
Accurate GHI and DNI data are essential for PV simulations as they determine the solar energy available for power generation, directly influencing energy yield predictions and system performance assessments. Reliable solar irradiance data ensures precise modeling of site-specific solar conditions, reducing uncertainties in system design, energy production estimates, and financial modeling.
To validate the accuracy of the Solargis satellite model data, sub-hourly values from the model have been compared with ground-measured data from high-quality, publicly available stations worldwide.
The ground reference data are sourced exclusively from high-accuracy instruments, which undergo a rigorous quality assessment to ensure their uncertainties remain within the tolerance limits of the instruments. This process ensures that the validation comparisons are reliable and robust.
Geographical scope | Global |
Data parameters | GHI, DNI |
Calculated indicators | Bias, RMSD |
GHI validation statistics
GHI represents the total solar radiation received per unit area on a horizontal surface. As the most widely used parameter in PV simulations, GHI is critical for solar power plants. It serves as the baseline measure of the solar resource available at a site, directly influencing energy yield predictions.
The table below summarizes the accuracy statistics of Solargis GHI data, validated against high-quality ground measurements from over 300 sites representing diverse climate conditions worldwide:
GHI | |
|---|---|
Number of validation sites | 320 |
Mean Bias for all sites | 0.5% |
Standard deviation | ±3.0% |
A mean bias of 0.5% for GHI indicates near-perfect agreement between satellite-based GHI data and ground measurements, showcasing the model's ability to accurately reflect total solar irradiance across diverse regions.
A standard deviation of ±3.0% for GHI signifies minimal variability, reinforcing the dataset's reliability and consistency.
The map below shows the sites at which the Solargis GHI time series was validated against the ground-measured data. By clicking on a site, its details can be displayed, including the basic site characteristics, and the validation statistics - bias, Root Mean Square Deviation (RMSD), and the number of valid data pairs.
DNI validation statistics
DNI measures the solar radiation received per unit area on a surface perpendicular to the sun’s rays, excluding diffuse radiation. Accurate and precise DNI data is essential for PV simulations, as it, together with GHI, enables the calculation of GTI. It is a specially critical parameter for specific PV technologies and applications that depend heavily on direct sunlight like CPV and CSP.
The table below shows the summary of the accuracy statistics of Solargis DNI data compared to high-quality ground measurements at more than 300 sites across all types of climates:
GHI | |
|---|---|
Number of validation sites | 235 |
Mean Bias for all sites | 2.2% |
Standard deviation | ±6.0% |
A mean bias of 2.2% for DNI shows good agreement between satellite-based DNI data and ground measurements, highlighting the model's reliability in capturing direct solar irradiance.
The ±6.0% standard deviation for DNI reflects moderate variability across sites, likely due to regional atmospheric complexities such as aerosols or cloud cover.
The map below shows the sites at which the Solargis DNI time series was validated against the ground-measured data. By clicking on a site, its details can be displayed, including the basic site characteristics, and the validation statistics - bias, Root Mean Square Deviation (RMSD), and the number of valid data pairs.
Conclusions
The validation exercise confirms that satellite-based GHI and DNI data are accurate and reliable for use in PV simulations, providing confidence in their ability to support the design, simulation, and optimization of PV systems.
With validation conducted at over 300 sites globally, the reference data demonstrates very good geographic and climatic coverage, enhancing the reliability and applicability of the results across diverse regions and conditions. The consistency across varying climates and regions ensures confidence in the data's suitability for PV system design and energy yield predictions worldwide.
The consistent RMSD (Root Mean Square Deviation) for hourly, daily, and monthly values reinforces the reliability of satellite-based data across temporal resolutions, supporting both short-term operational assessments and long-term energy yield predictions.
A detailed report with additional analysis and list of individual validation sites is also available here. Please note that due to rounding, there may be small differences in the data displayed on the map and the data presented in the report.