A comparative study of geographic information system tools has found that QGIS can provide more accurate estimates of rooftop photovoltaic potential in urban areas than ArcGIS Pro.
The study is titled ‘A comparative analysis of GIS-based tools for accurate photovoltaic potential prediction at urban scale.’
The research assessed two solar radiation tools, QGIS r.sun and ArcGIS Pro Raster Solar Radiation, using different weather inputs and time scales across three areas with varying urban characteristics. The results were validated against PVGIS, a widely used solar energy assessment tool.
The study found a stronger correlation between QGIS results and PVGIS data. QGIS r.sun also emerged as the more suitable option for large scale assessments because it can use raster inputs for the Linke turbidity factor and the diffuse to global radiation ratio. This allows the model to represent spatial differences in atmospheric conditions across urban, rural and mountainous areas.
Different results from similar tools
The two systems use broadly similar inputs, including solar radiation, slope, orientation and shading data. However, their calculations produced significantly different results.
ArcGIS Pro returned photovoltaic production values that were consistently higher than those produced by QGIS across all three study areas. The researchers linked QGIS results more closely to the Linke turbidity factor, which measures the effect of atmospheric conditions on incoming solar radiation. ArcGIS results showed a stronger relationship with the diffuse to global radiation ratio.
The differences were also evident in monthly estimates. QGIS produced a relatively consistent pattern, with photovoltaic production increasing during the first part of the year, reaching a peak in July and declining afterwards. ArcGIS Pro produced a less consistent pattern, with peaks recorded in June, May and October.
The findings highlight the importance of carefully calibrating weather inputs when estimating the solar potential of buildings. Although weather databases commonly provide both diffuse to global radiation and Linke turbidity data, the choice and calibration of these inputs can substantially affect the results.
Importance for urban solar planning
Accurate solar mapping is becoming increasingly important as cities seek to expand renewable energy generation without using additional land. Rooftop photovoltaic systems can be integrated into existing buildings, reducing the land requirements and environmental impacts associated with large solar farms.
Urban areas, however, present complex challenges. Buildings vary in height, orientation and roof shape, while nearby structures can create shadows that reduce electricity production. GIS tools can process detailed spatial data to identify suitable roofs and calculate their area, slope, orientation and exposure to sunlight.
The researchers say QGIS r.sun is particularly useful for broader assessments because it can represent changing atmospheric conditions across large and diverse territories. ArcGIS Pro offers some processing advantages, including the ability to use existing slope and orientation maps, which can reduce calculation time.
However, the ArcGIS Pro tool does not include reflected solar radiation in its calculations and does not allow the same level of adjustment to the viewshed algorithm as earlier versions of the software.
Need for better validation
The study recommends using site specific measurements to improve the reliability of solar potential estimates. Data from a single pyranometer could help calibrate incoming solar radiation and adjust values across a wider area.
Where it is not practical to install multiple measurement stations, tools such as PVGIS could help establish correction factors for areas without significant shading. The researchers note, however, that PVGIS does not account for local shading, meaning that unshaded areas would need to be identified separately.
Future work should also examine solar production at a finer time scale. The study relied mainly on aggregated annual and monthly data, but daily patterns are important because electricity demand and photovoltaic generation do not always occur at the same time.
Solar production generally peaks during daylight hours, while energy demand can increase during the evening and at night. Seasonal differences are also important, with demand often rising during winter while photovoltaic output is higher in summer.
The researchers further recommend incorporating additional factors into future photovoltaic potential calculations, including panel operating temperature, wind speed and the full life cycle of solar modules.
They say further field validation is needed to determine the actual accuracy of the models. This could involve collecting historical output data from existing solar installations or deploying dedicated monitoring systems across panels with different orientations and shading conditions.
As more detailed building data and higher resolution digital surface models become available, solar cadastres should become increasingly accurate. Initiatives such as the European Union’s Digital Building Stock Model are expected to support large scale mapping of rooftop solar potential and help cities identify the most productive locations for photovoltaic deployment.
Author: Bryan Groenendaal






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