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

• Published in: Energy (Oxford) Vol. 293; p. 130538
• Main Authors: Zang, Haixiang, Chen, Dianhao, Liu, Jingxuan, Cheng, Lilin, Sun, Guoqiang, Wei, Zhinong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.04.2024
• Subjects: Deep learning; Ground-based sky image; PV power forecasting; Spatial-temporal feature; Deep learning; PV power forecasting; Spatial-temporal feature; Ground-based sky image
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.130538

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. •An end-to-end image-based framework is developed for photovoltaic power forecasting.•Spatial-temporal features are extracted based on considering spatial-temporal feature interaction.•The correlation between image features and photovoltaic power is investigated.•A time series inference model based on progressive architecture is introduced.•The proposed framework outperforms benchmark frameworks for all case studies.