A smart solar panel system you can 3D print, build, and learn from – with the Arduino® UNO™ Q board
Solar energy is one of the most important technologies of our time, and understanding how it works can be… enlightening! Julián Caro Linares, Senior Field Technology Evangelist at Qualcomm Europe, built a project that helps learn about this topic with a hands-on approach: a fully functional, fully open-source smart solar panel system that adjusts to optimize solar exposure in real-time, built with UNO Q and a handful of Arduino® Modulino™ nodes. The best part? He explains every step so you can 3D print, assemble, and program it yourself, too!
A shining moment for UNO Q!
The system does a surprising amount for something you can build at home or in a classroom. Two servo motors move the solar panel continuously, orienting it toward the best position to capture energy from the sun based on a sky scan the board runs automatically. A live dashboard – served directly from the Linux side of UNO Q to any browser on your local network – shows panel orientation, a solar heatmap, real-time energy readings, infrared light levels, and local weather data. Just watching it update is pretty cool!
And the system doesn’t just chase the sun, it also protects itself. If temperatures climb too high, the panel adjusts to avoid damage. If a snowstorm or sandstorm is forecast, it moves to a protective vertical position. If it gets too dark to generate meaningful energy, it returns to a resting position and waits. And when dust or debris accumulates, it vibrates – just like NASA’s Mars rovers do – to shake itself clean.
All of this runs on UNO Q thanks to its dual-brain architecture: the Linux side handles the dashboard, weather data, and higher-level logic, while the real-time microcontroller reads sensors and drives the motors. The Modulino nodes do the sensing: Modulino Light reads both visible and infrared light, Modulino Thermo monitors temperature and humidity, Modulino Movement detects anomalous vibrations, and Modulino Vibro handles the self-cleaning function.
Everything was easily orchestrated through Arduino® App Lab, using three ready-to-use Bricks:
- weather_forecast: for an online API that always has the latest weather information;
- web_ui: to build and host the webpage;
- dbstorage_tsstore: to store and get data using a time series database.
Try it as a bright classroom project
Julián designed this explicitly as an educational tool, and it shows. The project is fully open-source – 3D files, code, and a complete step-by-step tutorial are all available on Arduino® Project Hub. On the hardware side, a small solar panel and two servo motors are all you need to add. For the software, Arduino App Lab handles the whole setup.
What makes it particularly compelling as a teaching tool is how many real concepts it touches at once. Students can explore renewable energy, sensor fusion, weather data integration, edge AI decision-making, real-time data visualization, and basic robotics – all within a single, coherent project that actually does something. Just observe when the panel tilts toward the sun, or switches to emergency mode before a storm: the logic behind is visible and understandable.
“It’s a very interesting project to do at home and to apply in the classroom,” he says, “to show how we can use UNO Q to solve real-life problems in actual solar panel installations.”
Check out the full tutorial on Arduino Project Hub.
In addition to the Arduino Store, UNO Q is available to order from DigiKey, Farnell, Mouser, Newark, RS Components, and Robu.in, along with our other authorized distributors and resellers.
Arduino, UNO, Modulino and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.
Facts Only
* Julián Caro Linares, Senior Field Technology Evangelist at Qualcomm Europe, built a smart solar panel system.
* The system is built with an Arduino UNO Q board and several Arduino Modulino nodes.
* Two servo motors continuously move the solar panel to optimize solar exposure based on a sky scan.
* A live dashboard displays panel orientation, a solar heatmap, real-time energy readings, infrared light levels, and local weather data.
* The system adjusts panel orientation if temperatures are too high or if a storm is forecast to provide protection.
* The system vibrates to clean dust or debris from the panel.
* The UNO Q features a dual-brain architecture: Linux handles the dashboard/logic, and the microcontroller reads sensors and drives motors.
* Modulino Light senses visible and infrared light levels.
* Modulino Thermo monitors temperature and humidity.
* Modulino Movement detects anomalous vibrations.
* Modulino Vibro manages the self-cleaning vibration function.
* Arduino App Lab was used with Bricks: weatherforecast, webui, and dbstoragetsstore.
* The project is open-source, with 3D files, code, and a tutorial available on Arduino Project Hub.
Executive Summary
Full Take
The presentation of this system emphasizes the fusion of hardware engineering, data science, and applied robotics to solve an environmental optimization problem. The structure highlights a potent pedagogical framework by weaving together disparate technical domains—renewable energy mechanics, sensor fusion, real-time decision-making (edge AI), and data visualization—into a single, tangible outcome. This pattern suggests that complex technological literacy is best cultivated when abstract concepts are made physically interactive. The layered architecture, separating the high-level logic on the Linux side from low-level control on the microcontroller, mirrors real-world system design principles, illustrating how complexity can be managed through distributed processing rather than monolithic computation. The inclusion of self-regulating protective mechanisms and autonomous cleaning features moves the project beyond simple data collection into genuine adaptive systems. The reliance on open-source tools like Arduino and App Lab promotes agency by democratizing the creation process, positioning the end-user not merely as a consumer but as an engineer capable of iterative design and problem-solving in real-world contexts. The implication is that true understanding stems from the capacity to build and observe functional systems rather than passively consuming information about them.
BRIDGE QUESTIONS: How can this integrated approach be scaled for large-scale grid management, and what are the sociotechnical implications when decentralized, self-regulating systems become commonplace? What external factors—such as material science constraints or infrastructure limitations—currently prevent immediate real-world deployment of these autonomous solar solutions? What role should educational institutions adopt in structuring curricula around this type of cross-disciplinary engineering practice?
