Short-term Solar Irradiance Prediction from Sky Images with a Clear Sky Model

Abstract

Integrating the solar power into the power grid system while maintaining its stability is essential for utilising such type of clean energy widely. It renders the solar irradiance (determining the solar power) forecasting a critical task. This paper tackles the problem of solar irradiance prediction from a history of sky image sequence. Most existing machine learning methods directly regress the solar irradiance values from a historical image sequence and/or solar irradiance observations. By contrast, we propose a novel deep neural network for short-term solar irradiance forecasting by leveraging a clear sky model. In particular, we build our network structure on the vision transformer to encode the spatial as well as the temporal information in the sky video sequence. We then aim to predict the solar irradiance residual from the learned representation by explicitly using a clear sky model. We evaluated our approach extensively on the existing benchmark datasets, such as TSI880 and ASI16. Results on the nowcasting task, namely estimation of the solar irradiance from the observations, and the forecasting task, which is up to 4-hour ahead-of-time prediction, demonstrate the superior performance of our method compared with existing machine learning algorithms.