Sky is the Limit: Exploring Solar Photovoltaic Nowcasting with Sky Images, Transfer Learning, and Data Augmentation

The increasing share of solar photovoltaic (PV) installations brings new challenges for grid stability, since the electricity generated is variable and depends on solar radiation and other meteorological factors. As previous research on nowcasting PV electricity generation has not simultaneously integrated sky images, temperature data, transfer learning, and data augmentation, we combined these approaches and evaluated various methodologies. Our machine learning models were trained on sky images and PV data derived from the SKIPP’D dataset at Stanford University and the SIRTA atmospheric observatory near Paris, enriched with temperature data from Weather Underground. The results indicate that the transfer learning model trained on augmented sky images from Stanford showed competing overall results and outperformed the current benchmark on sunny days with a root mean squared error of 0.570 on the SKIPP’D dataset. Our approach underscores the complex interactions between various predictive factors and highlights the need for sophisticated methods to enhance the nowcasting of PV power generation. Future research should explore alternative configurations and advanced architectures, such as vision transformer models, to further enhance the prediction accuracy and expand the contribution of machine learning to sustainable and environmentally friendly strategies.