When solar contractors are empowered with tools to make their work more efficient, it can help reduce the cost of solar. In particular, the development of techniques to design PV systems quickly and accurately without having to visit the site can reduce the time and costs associated with truck rolls. Remote system design has the potential to reduce the cost of solar by as much as $0.17/W, according to the U.S. National Renewable Energy Laboratory (NREL).
Designing a solar installation remotely requires an accurate 3D model of the roof and its surroundings. This ensures that the system is appropriate for the site and enables accurate modeling of the energy it will produce. There are multiple ways to create a representative 3D model of a site, including the use of LIDAR data, which is gathered by scanners that emit pulses of light energy (using a laser) at buildings and other objects in an area, and measure how long it takes for the pulse to return. However, LIDAR data is not available for all areas, and not always current where it is available.
Computer vision — the use of computers to interpret visual images — provides additional ways to remotely model a solar project site. With computer vision, widely available aerial and street view imagery can be used to extract information about the scene. Because of this, computer vision has the potential to transform the solar design process.
Making remote solar design more accurate
One exciting application of computer vision in remote design is the ability to accurately measure distances using images of a site. This is made possible by the mathematical technique of triangulation. Think back to your high school trigonometry class — you might recall that if you know the length of one side and two of the angles of a triangle, you can easily calculate the remaining two sides and angle. This process is based on the same rules.
Triangulation, commonly used in nautical navigation, allows you to determine the distance to an object if you know the direction from two locations. With this approach, computer vision can reconstruct the 3D shape of objects using images from multiple angles of a scene. If you form two rays extending out from the viewing locations, the rays will intersect at the location of the object.
Aurora’s Street View Ruler tool uses any satellite (such as Google or Bing) or aerial imagery (like Nearmap or imagery captured from a drone) and Google Street View imagery to provide those two different viewpoints needed to use triangulation. These imagery sources also provide information on the position of the camera that took the image, accurate within a few feet. With this data, computer vision allows solar project designers to extract 3D measurements from the scene — such as the slope of a roof or the heights of chimneys or trees — ensuring the accuracy of the site model.
NREL has analyzed Aurora’s computer vision measurements and validated their accuracy; roof slopes can be measured to within 2° and distances to within six inches. Over 1400 measurements per week are taken using Aurora’s computer vision measurement functionality, each one serving to enhance the accuracy of the design.
Speeding up the design process
Computer vision can also be used to speed up the tedious process of drawing a 3D model of a site. One of the most repetitive tasks when modeling commercial roofs is placing obstructions. Often, these roofs will have the same kind of obstruction in many places — such as skylights, vents or pipes. Using an approach called template matching, computer vision can automatically detect these instances in the imagery.
With Aurora’s automatic obstruction detection tool, the user can select a particular obstruction as a template to guide the identification of other similar instances. Aurora then sweeps over the image looking for similar obstructions. For every possible location of the obstruction, a score is generated based on how similar it is to the template. Then Aurora places obstructions at the highest-scoring locations. In this way, a time-consuming process can be reduced to just a few clicks.
“Aurora’s automated obstruction detection feature has been a huge help. We used to spend hours drawing out each individual skylight and AC unit, particularly on large data centers and warehouses, and this feature has easily cut our rooftop design time to a fraction of that,” explained Douglass Jordan, project manager of pre-construction services at SunPower.
As you can see, computer vision has huge implications for cutting costs in the solar industry. As a solar contractor, being able to accurately model a customer’s home or commercial site remotely saves your team time-intensive site visits. It can further speed up the remote design process by automating repetitive tasks, like identifying obstructions.
Aurora’s computer vision technology was developed in part through a grant from the Department of Energy’s SunShot Incubator program. The grant supported the development of these features as a means to reduce soft costs and advance the growth of the solar industry.
As the applications of computer vision evolve, Aurora’s computer vision team is working to continue to bring the latest innovations of the field to the industry, helping installers create PV projects quickly and accurately.
- Remote solar design can help save solar companies time and money by eliminating the need for onsite measurements. NREL has estimated this can reduce the cost of solar by as much as $0.17/W.
- Remote design requires the development of an accurate 3D model of the project site; computer vision — a field of computer science that teaches computers to interpret visual images — enhances accurate remote site modeling in multiple ways.
- One way that computer vision can increase the accuracy of 3D models of a solar installation site is by making it possible to take accurate site measurements from photos, including heights and roof slopes. This is made possible by using the mathematical technique of triangulation.
- Computer vision can also dramatically speed up the process of modeling a site by automatically detecting similar obstructions on the roof, eliminating the need for the designer to draw each one by hand.
This article was originally published on the Aurora Solar blog. If you’re interested in hearing more about Aurora’s computer vision team, please check out the original version of the article.