On vision transformer for ultra-short-term forecasting of photovoltaic generation using sky images

被引:4
|
作者
Xu, Shijie [1 ]
Zhang, Ruiyuan [2 ]
Ma, Hui [1 ]
Ekanayake, Chandima [1 ]
Cui, Yi [3 ]
机构
[1] Univ Queensland, Sch Informat Technol & Elect Engn, Brisbane 4072, Australia
[2] Hong Kong Univ Sci & Technol, Kowloon, Clear Water Bay, Hong Kong, Peoples R China
[3] Univ Southern Queensland, Brisbane, Australia
关键词
Deep learning; Forecasting; Image processing; Photovoltaic; Vision transformers; POWER; MODEL;
D O I
10.1016/j.solener.2023.112203
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:12
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