New approach for solar tracking systems based on computer vision, low cost hardware and deep learning
This work tackles cost and reliability issues in solar energy systems for renewable energy applications, though it appears incremental in combining existing technologies.
The researchers developed a low-cost solar tracking system using computer vision and deep learning to address cost and operational limitations, with preliminary tests at Plataforma Solar de Almeria showing it as a viable alternative to traditional systems.
In this work, a new approach for Sun tracking systems is presented. Due to the current system limitations regarding costs and operational problems, a new approach based on low cost, computer vision open hardware and deep learning has been developed. The preliminary tests carried out successfully in Plataforma solar de Almeria (PSA), reveal the great potential and show the new approach as a good alternative to traditional systems. The proposed approach can provide key variables for the Sun tracking system control like cloud movements prediction, block and shadow detection, atmospheric attenuation or measures of concentrated solar radiation, which can improve the control strategies of the system and therefore the system performance.