Short-Term Photovoltaic Power Prediction Based on Multi-Stage Temporal Feature Learning

Harnessing solar power is essential for addressing the dual challenges of global warming and the depletion of traditional energy sources. However, the fluctuations and intermittency of photovoltaic (PV) power pose challenges for its extensive incorporation into power grids. Thus, enhancing the precision of PV power prediction is particularly important. Although existing studies have made progress in short-term prediction, issues persist, particularly in the underutilization of temporal features and the neglect of correlations between satellite cloud images and PV power data. These factors hinder improvements in PV power prediction performance. To overcome these challenges, this paper proposes a novel PV power prediction method based on multi-stage temporal feature learning. First, the improved LSTM and SA-ConvLSTM are employed to extract the temporal feature of PV power and the spatial-temporal feature of satellite cloud images, respectively. Subsequently, a novel hybrid attention mechanism is proposed to identify the interplay between the two modalities, enhancing the capacity to focus on the most relevant features. Finally, the Transformer model is applied to further capture the short-term temporal patterns and long-term dependencies within multi-modal feature information. The paper also compares the proposed method with various competitive methods. The experimental results demonstrate that the proposed method outperforms the competitive methods in terms of accuracy and reliability in short-term PV power prediction.