TL
TL;DR
The proposed anticipative transformer-based model effectively learns to attend only to relevant features in images in order to forecast irradiance, and captures long-range dependencies between sky images to achieve a forecasting skill of 21.45 % on a 15 minute ahead prediction.
Abstract
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.
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