To utilize deep learning towards real-time energy performance detection for solar modules
Technology & Design
Mitacs
Description
Conversion of solar energy using PV panels faces challenges due to snow accumulation on PV surface in cold regions. Despite existing methods to assess this impact, there remains a gap in real-time detection and accurate quantification of the energy loss. This project introduces a novel deep learning-based method for detecting snow coverage on PV panels for maximizing solar energy conversion. The model achieved a Dice score of 0.81 when trained on a diverse dataset of PV images, achieving a 44% improvement over conventional computer vision methods. Energy losses from snow on solar panels showed the model's predictions align closely with ground-truth data, achieving an error under 5% in snow coverage prediction. The maximum energy loss was 0.23 kWh from a large PV array system covering 117 m2 area. Analysis of the impact of snow coverage duration on energy loss showed a saving potential of 0.13 kWh/m2 using timely clearing of snow coverage.
