Ultra-Short-Term Photovoltaic Power Forecasting Based on an Improved Spatio-Temporal Joint Attention Mechanism

This paper proposes a novel forecasting model termed U-Client, which integrates parallel cross-temporal and cross-variable attention branches with an adaptive gated fusion mechanism for ultra-short-term photovoltaic (PV) power forecasting. First, meteorological features are screened using the Pearson correlation coefficient to reduce input dimensionality. Second, parallel cross-temporal and cross-variable attention branches are designed to extract long-range temporal trends and nonlinear interaction features among meteorological variables, respectively. Third, a gating mechanism is introduced to adaptively fuse the two types of features based on input conditions. Finally, a linear module is combined to generate the final forecasting results. Experiments based on measured datasets from a photovoltaic station in Ningxia, China, demonstrate that the proposed U-Client model outperforms classical models such as Long Short-Term Memory (LSTM) and Informer across all evaluation metrics for 1–4 step forecasting tasks. Ablation studies and statistical significance tests further verify the effectiveness of each component. The proposed model provides reliable support for ultra-short-term power dispatching in new-type power systems.