To model snow related energy losses for solar modules using image processing and deep learning
Energy & Environment
Mitacs
Description
Energy loss resulting from partial or full snow coverage on solar modules, such as PVs, poses serious challenges to the efficiency of renewable sources in cold climates. This project introduces new methods to quantify the impact of snow on installed PV panels using image processing and deep learning techniques. A set of 44 PV panels in four different arrays were examined in the experiment through per-pixel analysis that was correlated with various performance indicators. Energy production was computed where the estimated lowest average was 0.04 kWh/panel, due to high uncovered PV ratio with an average value of 84.1%. The validation with alternative methods resulted with an average deviation of 1.21%. Overall, remote monitoring of PV panels can be predicted to enhance energy production performance, while using deep learning-aided digital photography to track accumulation.
