Solar irradiance anticipative transformer
Solar irradiance anticipative transformer
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.
Mercier, Thomas M.
5ada2b38-a326-458c-93f3-90d97d097592
Rahman, Tasmiat
e7432efa-2683-484d-9ec6-2f9c568d30cd
Sabet, Amin
2f22efe3-7a1b-48ac-b8ce-d47a8e78aeb3
14 August 2023
Mercier, Thomas M.
5ada2b38-a326-458c-93f3-90d97d097592
Rahman, Tasmiat
e7432efa-2683-484d-9ec6-2f9c568d30cd
Sabet, Amin
2f22efe3-7a1b-48ac-b8ce-d47a8e78aeb3
Mercier, Thomas M., Rahman, Tasmiat and Sabet, Amin
(2023)
Solar irradiance anticipative transformer.
In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
IEEE.
10 pp
.
(doi:10.1109/CVPRW59228.2023.00200).
Record type:
Conference or Workshop Item
(Paper)
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.
This record has no associated files available for download.
More information
Published date: 14 August 2023
Identifiers
Local EPrints ID: 492460
URI: http://eprints.soton.ac.uk/id/eprint/492460
PURE UUID: 573b8ba8-dfb0-4dd9-984c-b50981efafce
Catalogue record
Date deposited: 29 Jul 2024 16:39
Last modified: 30 Jul 2024 01:47
Export record
Altmetrics
Contributors
Author:
Thomas M. Mercier
Author:
Tasmiat Rahman
Author:
Amin Sabet
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics