Diffusion Models for High-Resolution Solar Forecasts
This provides improved probabilistic forecasts for solar energy applications, but it is an incremental application of an existing method to a new domain.
The paper tackled the problem of accurately modeling uncertainty in high-dimensional solar irradiance forecasts by applying score-based diffusion models to super-resolve coarse weather predictions, achieving unprecedented resolution, speed, and accuracy in day-ahead probabilistic forecasts.
Forecasting future weather and climate is inherently difficult. Machine learning offers new approaches to increase the accuracy and computational efficiency of forecasts, but current methods are unable to accurately model uncertainty in high-dimensional predictions. Score-based diffusion models offer a new approach to modeling probability distributions over many dependent variables, and in this work, we demonstrate how they provide probabilistic forecasts of weather and climate variables at unprecedented resolution, speed, and accuracy. We apply the technique to day-ahead solar irradiance forecasts by generating many samples from a diffusion model trained to super-resolve coarse-resolution numerical weather predictions to high-resolution weather satellite observations.