Swedish researchers from Uppsala University and Becquerel Sweden have developed a method to enhance aerial imaging with data generated by light detection and ranging (LiDAR) systems in order to improve estimates of installed solar energy system (SES) capacity. The azimuth modeling is reportedly accurate within 3 degrees for between 91% and 95% of the systems evaluated.
The researchers showed how LiDAR data* can be used to estimate solar PV and solar thermal capacity for power planning at a regional or substation level. “Recent advances in identifying and segmenting SES, using aerial imagery, point to a logical next step, enhancing the modeling of tilts and azimuths, as these influence the power output significantly,” they said.
“Commonly, the orientation is assumed to be optimal, or similar to nearby systems with known tilt and azimuth. This is fine if studying a larger area, but on a substation level, deviations from the real power output may be significant,” David Lingfors, corresponding author of the research, told pv magazine.
The researchers developed the model to handle a variety of special cases, such as cases where linear regression is not feasible; or where there is an insufficient number of LiDAR points; or ground-mounted systems and vertical systems; or bi-directional rooftop PV cases; or those with an abnormally high tilt and poor regression metrics.
To find the orientation, the polygons representing the SES in aerial images were orthorectified to match the LiDAR data, followed by linear regression with Matlab’s robustfit function.
The method was evaluated for the ground truth of the azimuth of 3,500 Swedish solar energy systems previously identified from aerial images using convolutional neural network (CNN) technology with a manually derived ground truth azimuth dataset.
The researchers found that for 91%-95% of the systems, the model accurately estimated the azimuth within a margin of 3 degrees. Additionally, it found that the distribution of azimuths was narrower for solar thermal systems than for PV systems. Furthermore, a ground truth of the tilt for a subset of 39 systems gave a mean absolute error of 3.6°, noted the researchers.
“Surprisingly, we found that the azimuth is often far from optimal. The distribution is flatter than expected with 10% of panel area found to be facing more north than south in our study, which results in a low yield for northern latitudes. This shows that the often used assumption of optimal azimuth is misleading,” said Lingfors.
Concluding that the model can provide more accurate PV metadata, the researchers said that the model could enable aggregators and grid operators to make more precise PV power simulations and forecasts to improve grid operation and planning.
The work is described in “Deriving the orientation of existing solar energy systems from LiDAR data at scale,” published by Solar Energy.
When asked about what research is upcoming, Johan Lindahl, co-corresponding author, told pv magazine, “In the pipeline is an article about the accuracy of a PV power simulation method that utilized the orientation data to simulate the individual PV power generation of all distributed PV systems within an area with an hourly resolution.”
“After that, we will present a study that has quantified the PV power generation orientation smoothing effects on low-voltage substation levels, and analyzed how much additional grid capacity can be made available if grid operators would take into account orientation smoothing effects in the grid planning phase,” he added.
*This article was amended on 31 March 2025 to correct the description of how LiDAR data was used.
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