Solar Irradiance Anticipative Transformer
Thomas M. Mercier Tasmiat Rahman Amin Sabet
This paper proposes an anticipative transformer-based model for short-term
solar irradiance forecasting. Given a sequence of sky images, our proposed
vision transformer encodes features of consecutive images, feeding into a
transformer decoder to predict irradiance values associated with future unseen
sky images. We show that our model effectively learns to attend only to
relevant features in images in order to forecast irradiance. Moreover, the
proposed anticipative transformer captures long-range dependencies between sky
images to achieve a forecasting skill of 21.45 % on a 15 minute ahead
prediction for a newly introduced dataset of all-sky images when compared to a
smart persistence model.