Predicting future solar irradiance is critical for the efficiency and integration of solar power plants. Cloud dynamics, which significantly influence solar irradiance, play a central role in this process. Developing advanced solar forecasting models is essential to address the challenges of the energy transition and mitigate climate change. This Master's thesis focuses on optimization and further development of our data-driven solar forecasting models using ground-based sky images and sensor data. A key requirement to obtain spatially-resolved information on the effect of clouds on solar irradiance is their geolocation. While detecting clouds in images has been improved significantly, exact geolocation remains a challenge. Leveraging available high-quality datasets, this research will explore data preprocessing techniques, innovative training methodologies and model validation.
If this sounds like an exciting opportunity for you, please contact us! Yann Fabel (yann.fabel@dlr.de + 49 2203 6011 038). Please enclose supporting documents for the above points with your application.