Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26757
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dc.contributor.authorKang, Z-
dc.contributor.authorXue, J-
dc.contributor.authorLai, CS-
dc.contributor.authorWang, Y-
dc.contributor.authorYuan, H-
dc.contributor.authorXu, F-
dc.date.accessioned2023-06-30T16:37:11Z-
dc.date.available2023-06-30T16:37:11Z-
dc.date.issued2023-06-15-
dc.identifierORCID iDs: Chun Sing Lai https://orcid.org/0000-0002-4169-4438; Haoliang Yuan https://orcid.org/0000-0001-5167-226X.-
dc.identifier.citationKang, Z. et al. (2023) 'Vision Transformer-Based Photovoltaic Prediction Model', Energies, 16 (12), 4737, pp. 1 - 14. doi: 10.3390/en16124737.en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/26757-
dc.descriptionData Availability Statement: The data presented in this study are available upon request from the corresponding authors.en_US
dc.description.abstractCopyright © 2023 by the authors. Sensing the cloud movement information has always been a difficult problem in photovoltaic (PV) prediction. The information used by current PV prediction methods makes it challenging to accurately perceive cloud movements. The obstruction of the sun by clouds will lead to a significant decrease in actual PV power generation. The PV prediction network model cannot respond in time, resulting in a significant decrease in prediction accuracy. In order to overcome this problem, this paper develops a visual transformer model for PV prediction, in which the target PV sensor information and the surrounding PV sensor auxiliary information are used as input data. By using the auxiliary information of the surrounding PV sensors and the spatial location information, our model can sense the movement of the cloud in advance. The experimental results confirm the effectiveness and superiority of our model.en_US
dc.format.extent1 - 14-
dc.format.mediumElectronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherMDPIen_US
dc.rightsCopyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.subjectphotovoltaic predictionen_US
dc.subjectvisual transformeren_US
dc.subjectauxiliary informationen_US
dc.titleVision Transformer-Based Photovoltaic Prediction Modelen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.3390/en16124737-
dc.relation.isPartOfEnergies-
pubs.issue12-
pubs.publication-statusPublished online-
pubs.volume16-
dc.identifier.eissn1996-1073-
dc.rights.holderThe authors-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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