A novel sky image-based solar irradiance nowcasting model with convolutional block attention mechanism

Abstract

Global horizontal irradiance (GHI) is a crucial factor impacting photovoltaic (PV) production, and is required for accurate real-time photovoltaic power forecasting. And it is a new effective solution to obtain the real-time GHI by the sky images because the GHI is mainly affected by the cloud cover and cloud motion. Therefore, the research proposes a unique artificial intelligence approach for real-time GHI forecasting (‘nowcasting’) based on sky images, which can significantly enhance GHI forecasting accuracy on cloudy sky days. First, a new nowcasting model with convolutional block attention module (CBAM) is proposed, which is based on Visual Geometry Group (VGG) networks. Then, taking the local cloud cover (LCC) as a numerical feature, we coupled it with the cloud feature in the sky image to improve the forecasting performance of the GHI model. Finally, to verify the effectiveness and advantages of the proposed method, when compared to state-of-the-art methods, such as Sun’s model, Jiang’s model, and others, the proposed method outperforms them in GHI forecasting as demonstrated by the 11.67% nRMSE, 7.97% nMAE, 27.69% MAPE, and 0.91 CORR results on the ASI-16 dataset.

Keywords

• Solar irradiance forecasting
• Visual geometry group networks
• Convolutional block attention module
• Nowcasting model