On vision transformer for ultra-short-term forecasting of photovoltaic generation using sky images
Shijie Xu, Ruiyuan Zhang, Hui Ma, Chandima Ekanayake, Yi Cui
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.