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

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.