Solar Irradiance Anticipative Transformer
Authors: Mercier, T.M., Rahman, T. and Sabet, A.
Journal: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume: 2023-June
Pages: 2065-2074
eISSN: 2160-7516
ISSN: 2160-7508
DOI: 10.1109/CVPRW59228.2023.00200
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.
https://eprints.bournemouth.ac.uk/39066/
Source: Scopus
Solar Irradiance Anticipative Transformer
Authors: Mercier, T.M., Rahman, T. and Sabet, A.
Editors: O’Conner, L.
Pages: 2065-2074
Publisher: IEEE
Place of Publication: New York, NY
ISBN: 9798350302493
ISSN: 2160-7508
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.
https://eprints.bournemouth.ac.uk/39066/
Source: BURO EPrints