On Vision Transformer for Ultra-short-term Forecasting of Photovoltaic Generation Using Sky Images
Article
Article Title | On Vision Transformer for Ultra-short-term Forecasting of Photovoltaic Generation Using Sky Images |
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ERA Journal ID | 4074 |
Article Category | Article |
Authors | Xu, Shijie, Zhang, Ruiyuan, Ma, Hui, Ekanayake, Chandima and Cui, Yi |
Journal Title | Solar Energy |
Journal Citation | 267 |
Article Number | 112203 |
Number of Pages | 12 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0038-092X |
1471-1257 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.solener.2023.112203 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0038092X2300837X |
Abstract | An accurate photovoltaic (PV) generation forecasting is important for grid scheduling and dispatching. However, ultra-short-term PV generation forecasting is rather challenging because weather conditions may change significantly in a short time period largely due to the dynamics and movement of clouds above a solar PV farm. For monitoring clouds above the solar PV farm, ground-based whole-sky cameras (Sky-Imagers) have been installed. This paper develops a novel cloud image-based ultra-short-term forecasting framework. Within the framework, an integration of the Vision Transformer (ViT) model and the Gated Recurrent Unit (GRU) encoder is designed for the high-dimensional latent feature analysis. A Multi-Layer Perception (MLP) is employed to generate the one-step-ahead PV generation forecasting. Numeric experiments are conducted using real-world solar PV datasets. The results show that the proposed framework and algorithms can achieve higher accuracy compared to several baseline methods for ultra-short-term PV generation forecasting. |
Keywords | Deep learning; Forecasting; Image processing; Photovoltaic; Vision transformers |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400803. Electrical energy generation (incl. renewables, excl. photovoltaics) |
Byline Affiliations | University of Queensland |
Hong Kong University of Science and Technology, China | |
University of Southern Queensland |
https://research.usq.edu.au/item/z34y8/on-vision-transformer-for-ultra-short-term-forecasting-of-photovoltaic-generation-using-sky-images
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