The rapid shift toward renewable energy has positioned solar power as a key player in reducing carbon emissions. Yet, the inherent variability of solar irradiance, in particular abrupt fluctuations caused by local cloud movements, poses significant challenges for grid stability and hinders the large-scale adoption of this technology. Accurate short-term forecasting of solar irradiance becomes crucial to mitigate these issues.
Traditional forecasting methods, such as Numerical Weather Prediction, lack the spatial and temporal resolution required to predict these sudden changes in real time. To address this gap, we propose a novel generative AI pipeline that employs diffusion models to predict future sky conditions using ground-based sky images. These predicted images are processed by a convolutional neural network to forecast the solar irradiance reaching the ground.
We benchmark our approach against existing machine learning and traditional forecasting techniques, showing promising improvements in predicting short-term irradiance, particularly during dynamic situations. Additionally, we explore the role of stochasticity in diffusion models and develop a probabilistic framework that generates full probability distributions rather than single-point predictions. This allows our method not only to deliver robust predictive performance but also to quantify the uncertainty associated with each prediction.