Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction

Abstract

Accurate photovoltaic (PV) power forecasting is crucial to ensure the safety and stability of power systems, given the penetration of solar energy. Extracting spatial-temporal features from ground-based sky images can greatly improve ultra-short-term PV power forecasting. Previous studies have primarily focused on extracting holistic spatial-temporal features from sky images without considering their interaction, leading to a loss of partial critical features that restricts the improvement of forecasting performance. Hence, this study proposes a novel framework considering the interaction of spatial-temporal features for ultra-short-term PV power forecasting. First, a two-stream network is used to extract spatial and temporal features separately from sky images, aiming to eliminate the negative impact of spatial-temporal feature interaction. Then, a gate unit is employed to fuse the extracted features adaptively. Subsequently, a PV-guided attention mechanism is proposed to enhance forecasting performance by identifying dominant regions within the fused feature map. Last, a time series inference model based on progressive architecture is proposed to forecast future PV power. Comparative results demonstrate that the proposed framework outperforms benchmark frameworks and exhibits higher generalization and robustness in ultra-short-term PV power forecasting.