Effective power management begins at the power source. For solar panels, it sounds simple enough: Just point the panels to enable them to capture the maximum possible number of impinging photons and use mechanical solar tracking if feasible. In “open” settings where the sky is in full or nearly full view, this isn’t too hard to do, in principle.
It’s a very different and more challenging story in dense urban settings where nearby buildings, shadows, reflections from glass and buildings, and other nearby realities have a direct impact on the light path. This is especially true for smaller, fixed panels that are used for small charging stations, signage, kiosks, and similar localized (not grid-connected) situations. That simple plan to “point it at the sun’s maximum” is often not the best solution in a real-world setting.
As a result, various tools are available to assist in assessing the actual intended setting and achieving maximum solar collection there. Assessing the illumination (irradiance) of a panel is typically performed using a 3D model, but those fail to capture small nearby structures that impact the path and net irradiance.
Therefore, along with improving the efficiency of the solar panel itself (hard to do), power conversion and management electronics, and overall power path, it makes sense to also improve the up-front energy capture.
Recognizing the need, a research team in the Engineering School at Columbia University (New York) has devised a system where a single spherical photo at a given target site can be used to determine the year-round optimal orientation and angling of the panel. Their approach takes into account the sun’s angle, nearby shading, diffuse light from a partially visible sky, the reflection off windows, and even the façade of buildings—all characteristics of the so-called “urban canyon.” Researchers claimed their system can boost solar capture from 10% to as high as 30%—certainly not a trivial amount—and, even better, it’s a cost-free improvement.
The technology uses shadows and lines in the image to find the orientation of the camera, then segments the image to determine how various elements in the frame will contribute to overall power generation (Figure 1).
Standard methods can produce large errors because they fail to account for variations in the brightness of a section of sky visible to a panel throughout the day and year, as well as light reflected off surrounding buildings. The team found that these reflections account for roughly 12% of a panel’s total annual energy, on average.
Their research showed that a single spherical image can be used to find the best fixed orientation of a panel. The basic insight behind the new method is that a photograph taken from a panel’s location encodes an enormous amount of information. Shadows in the image reveal the direction of the sun, and straight lines in the architecture indicate the direction of gravity.
This information is used to find the image’s orientation with respect to the planet. A segmentation algorithm reveals the portion of the sky that is visible to the panel, and the appearance of buildings around the panel provides cues related to the spatial layout of the buildings and the materials of which they are made (Figure 2). 
From there, the laptop-based system forecasts how the sun will move through that visible sky across the day and year, estimates how much light will bounce off surrounding buildings onto the panel, and draws on historical weather data to extend the forecast across cloudy and overcast conditions, not just clear days (Figure 3). 
The team used a physically based renderer to simulate the scene irradiance as a function of the sun’s position. Figure 4(a) shows the simulated scene irradiance at one such location over the course of a day. Even though there are many objects casting shadows and producing reflections within the canyon, the scene irradiance varies smoothly with time.
Based on those observations, researchers developed a neural network to predict the scene irradiance of a panel from a single image. The network contains a vision transformer backbone and a multilayer perceptron (MLP) head that outputs the scene irradiance for any sun position (Figure 5).
A common rule of thumb states that the best fixed orientation of a panel is pointing toward the equator with a zenith angle equal to the panel’s latitude. This assumes the panel has a clear view of the sky, which is rarely the case in urban settings. Instead, researchers showed that a single spherical image taken near an existing panel can be used to identify the best fixed panel orientation, which may differ substantially from the above rule of thumb. They also validated their approach using actual irradiance measurements in urban canyons.
The team’s work is detailed in a lengthy (23 pages!) but highly readable paper with the short, direct, and enticing title “Forecasting solar energy using a single image” published in Solar Energy. It covers the problem, solution concept, alternatives, situation analysis, discussion of 3D geometries, urban canyon realities, optical considerations, and much more. While that formal posting is behind a paywall, an open version is posted at arXiv [a free, open-access, non-profit repository that hosts scholarly research papers (known as preprints) before they undergo formal peer review and publication in a scientific journal].
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RELATED TOPICS: IRRADIANCE, NEURAL NETWORKS, PHOTONS, RENEWABLE ENERGY, SOLAR ENERGY, SOLAR PANELS, URBAN CANYON

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Bill Schweber is an electronics engineer who has written three textbooks on electronic communications systems, as well as hundreds of technical articles, opinion columns, and product features. In past roles, he worked as a technical website manager for multiple EE Times sites and as both Executive Editor and Analog Editor at EDN. At Analog Devices, he was in marketing communications; as a result, he has been on both sides of the technical PR function, presenting company products, stories, and messages to the media and also as the recipient of these. Prior to the marcom role at Analog, Bill was Associate Editor of its respected technical journal, and also worked in its product marketing and applications engineering groups. Before those roles, he was at Instron Corp., doing hands-on analog- and power-circuit design and systems integration for materials-testing machine controls. He has a BSEE from Columbia University and an MSEE from the University of Massachusetts, is a Registered Professional Engineer, and holds an Advanced Class amateur radio license. He has also planned, written, and presented online courses on a variety of engineering topics, including MOSFET basics, ADC selection, and driving LEDs.
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