Automatic Inspection of Utility Scale Solar Power Plants using Deep Learning
This addresses the challenge of monitoring large solar plants for operators, though it appears incremental as it applies existing deep learning methods to a new domain.
The paper tackles the problem of costly monitoring of utility-scale solar power plants by using deep learning to automatically analyze drone footage, demonstrating it as a quick and reliable alternative that can save significant power and impact the developing world.
Solar energy has the potential to become the backbone energy source for the world. Utility scale solar power plants (more than 50 MW) could have more than 100K individual solar modules and be spread over more than 200 acres of land. Traditionally methods of monitoring each module become too costly in the utility scale. We demonstrate an alternative using the recent advances in deep learning to automatically analyze drone footage. We show that this can be a quick and reliable alternative. We show that it can save huge amounts of power and the impact the developing world hugely.