Ultrashort-Term Multistep Photovoltaic Power Forecasting Based on Whole-Sky Images and Conditional Temporal Fusion

Abstract Photovoltaic (PV) power output is strongly affected by the rapid evolution of clouds. Traditional methods that rely only on historical power sequences or low-spatiotemporal-resolution meteorological data struggle to accurately characterize rapid fluctuations and ramp events within a 10–20 min horizon, where PV power rises or drops sharply over a short period. To address this issue, this paper develops an ultra-short-term multistep forecasting framework centered on whole-sky images (WSIs) and recent PV observations. We first design a static image model and a static hybrid model based on the large-small network (LSNet) to mine spatial structural information from single-frame WSIs and use power-conditioned cross-attention to alleviate the representation ambiguity that similar sky appearances may correspond to different output levels. We further propose a dynamic image learning model based on LSNet and a temporal convolutional network, which models short-term cloud-motion evolution on top of frame-wise spatial encoding and introduces a multiscale radiative transmission index (RTI) and a ramp-aware multistep training objective to enhance sensitivity to rapid cloud-occlusion progression and high-risk samples. Experiments on the public Sky Images and Photovoltaic Power Generation Dataset (SKIPP’D) show that the proposed method outperforms the persistence model, single-layer neural network, long short-term memory, and recent baselines such as RTI-Net, SolNet, and SolarNexus in both general forecasting and ramp-event scenarios. Under general forecasting conditions, our method achieves a root mean square error (RMSE) of 27.48 and a mean absolute percentage error (MAPE) of 9.07, which are 29.1% and 28.1% lower than those of the smart persistence model (SPM), respectively. Under ramp-event scenarios, balanced performance reaches 76.84%, a 52.4% improvement over SPM, while ramp-event RMSE and ramp-event MAPE are reduced to 58.93 and 17.11, corresponding to decreases of 47.7% and 62.2%, respectively. These results indicate that jointly exploiting the spatial structure of sky images, recent power context, and ramp-oriented modeling strategies can substantially improve the accuracy and dispatch usefulness of ultra-short-term multistep PV power forecasting.