Skip to main content

Research Repository

Advanced Search

Transfer learning in very-short-term solar forecasting: Bridging single site data to diverse geographical applications

Zhang, Liwenbo; Wilson, Robin; Sumner, Mark; Wu, Yupeng

Transfer learning in very-short-term solar forecasting: Bridging single site data to diverse geographical applications Thumbnail


Authors



Abstract

Over the past decade, the rapid growth of solar energy penetration has posed significant challenges for grid balancing and scheduling, heightening the need for accurate and efficient short-term solar forecasting. While deep learning models have shown promise in improving forecasting accuracy, previous studies have often focused on data from specific sites, limiting their generalisability across different climatic and geographical conditions. This study addresses this limitation by employing a multimodal self-attention deep model, trained under the dry and clear climate conditions of Folsom, California, and integrating various transfer learning techniques. We examine the transferability of this model to a new dataset from Nottingham, UK, characterised by humid and rainy conditions. Specifically, we compare different transfer methods based on model architecture and validate performance with limited target site data (equivalent to two weeks of data). The model's expertise can be effectively transferred, reducing the required data for successful model training by 80% (from four months to two weeks). Simulations under realistic scenarios demonstrate that the model, trained with just two weeks of data from the deployment site, achieved performance surpassing the baseline. This work demonstrates the feasibility of transferring deep learning models for solar forecasting across diverse climatic conditions, significantly reducing the data and time needed for model adaptation and deployment. This has the potential to enhance the reliability and efficiency of solar energy integration into power grids globally.

Citation

Zhang, L., Wilson, R., Sumner, M., & Wu, Y. (2025). Transfer learning in very-short-term solar forecasting: Bridging single site data to diverse geographical applications. Applied Energy, 377, Article 124353. https://doi.org/10.1016/j.apenergy.2024.124353

Journal Article Type Article
Acceptance Date Aug 28, 2024
Online Publication Date Oct 3, 2024
Publication Date Jan 1, 2025
Deposit Date Oct 8, 2024
Publicly Available Date Oct 10, 2024
Journal Applied Energy
Print ISSN 0306-2619
Electronic ISSN 0306-2619
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 377
Article Number 124353
DOI https://doi.org/10.1016/j.apenergy.2024.124353
Keywords Solar energy; Very-short-term solar forecasting; Computer vision; Deep learning; Vision transformer; Transfer learning; Sky images
Public URL https://nottingham-repository.worktribe.com/output/40291294
Publisher URL https://www.sciencedirect.com/science/article/pii/S0306261924017367?via%3Dihub
Additional Information This article is maintained by: Elsevier; Article Title: Transfer learning in very-short-term solar forecasting: Bridging single site data to diverse geographical applications; Journal Title: Applied Energy; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.apenergy.2024.124353; Content Type: article; Copyright: © 2024 The Authors. Published by Elsevier Ltd.