At BroutonLab, we worked on several projects where we applied machine learning and computer vision techniques to develop smart monitoring software for solar panels.
Solar panels are gaining more popularity in various sectors thanks to the benefits they offer. They reduce ever-rising electricity costs and help protect the environment.
Despite being easy to maintain, solar panels wear out due to weather and mishaps. The silicon semiconductors react to the temperature and humidity changes and can de grade even with regular cleaning. So you have to inspect the solar panels and watch their electrical input regularly to avoid failures. It can be quite a daunting task, especially if you have a lot of them to observe.
Reduce downtime by detecting failures quickly
Save on repair costs by implementing a proactive maintenance strategy
Reduce the volume of manual work. Analyze and spot even minor faults automatically with prompt alerts
Data Scientists at BroutonLab developed an image processing engine to monitor failures in solar panels.
We used OpenCV and AI-powered object detection techniques to achieve the maximum accuracy of the software.
Our software can detect anomalies and faults by analyzing RGB and infrared images. The images are taken by drones and other unmanned flying vehicles to check the panels in real-time.
The AI-powered engine timely detects anomalies in solar panels and requires minimum manpower to monitor them in real-time.
