A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting

Jingxuan Liu, Haixiang Zang, Lilin Cheng, Tao Ding, Zhinong Wei and Guoqiang Sun

Applied Energy, 2023, vol. 342, issue C, No S030626192300524X

Abstract: The development of solar energy is crucial to combat the global climate change and fossil energy crisis. However, the inherent uncertainty of solar power prevents its large-scale integration into power grids. Although various sky-image-derived modeling methods exist to forecast the variations of solar irradiance, few focus on fully utilizing the coupling correlations between sky images and historical data to improve the forecasting performance. Therefore, a novel multimodal-learning framework is proposed for forecasting global horizontal irradiance (GHI) in the ultra-short-term. First, the historical and empirically estimated clear-sky GHI are encoded by Informer. Then, the ground-based sky images are transformed into optical flow maps, which can be handled by Vision Transformer. Subsequently, a cross-modality attention method is proposed to explore the coupling correlations between the two modalities. Last, a generative decoder is used to implement multi-step forecasting. The experimental results show that the proposed method achieves a normalized root mean square error (NRMSE) of 4.28% in 10-min-ahead forecasting. Several state-of-the-art methods are also used for comparisons. The experimental results show that the proposed method outperforms the benchmark methods and exhibits higher accuracy and robustness in ultra-short-term GHI forecasting.

Keywords: Solar irradiance forecasting; Multimodal-learning; Transformer; Ground-based sky image (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030626192300524X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:342:y:2023:i:c:s030626192300524x

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2023.121160

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().