Solar Energy Management System using Computer Vision
The solar energy industry is experiencing rapid growth and innovation, and machine learning is playing a key role in driving this trend. Solar energy plays a crucial role in the sustainability initiative providing a clean, renewable, and cost-effective source of power. The adoption of solar energy usage can help to address climate change, improve energy security, and provide access to electricity in remote areas. This growth is fueled by the increasing adoption of machine learning and artificial intelligence technologies, which are helping organisations in the solar energy industry to more accurately predict and optimise the performance of their solar panels. These models can effectively analyse images of solar panels to detect and diagnose issues such as microcracks, “snail trails”, broken glass, hot spots, dust build-up and other defects that may impact their performance. Building and deploying these models can be a complex process, requiring the use of multiple tools and a high level of technical expertise.
This technology offer is a customisable end-to-end MLOps platform that is capable of streamlining the process and makes it easier for teams to build custom computer vision models specifically for solar energy monitoring and optimisation. With this platform, teams can quickly and easily convert their data into working models with enterprise-standard practices, ensuring the accuracy and reliability of their solar energy monitoring systems.
The technology owner is keen to do R&D collaboration with organisations looking to improve and optimise the overall design and integration of solar energy systems.