Sky Image Prediction Using Generative Adversarial Networks for Solar Forecasting (Papers Track)
Yuhao Nie (Stanford University); Andea Scott (Stanford University); Eric Zelikman (Stanford University); Adam Brandt (Stanford University)
Large-scale integration of solar photovoltaics (PV) is challenged by high variability in its power output, mainly due to local and short-term cloud events. To achieve accurate solar forecasting, it is paramount to accurately predict the movement of clouds. Here, we use generative adversarial networks (GANs) to predict future sky images based on past sky image sequences and show that our trained model can generate realistic future sky images and capture the dynamics of clouds in the context frames. The generated images are then evaluated for a downstream solar forecasting task; results show promising performance.