精确的光伏功率超短期概率预测可降低光伏出力不确定性对电力系统的影响，为电网决策调度提供可靠依据。为此，提出了一种基于深度学习技术的光伏出力超短期预测方法。首先，基于历史光伏数据特性进行了特征相关性分析及K-means++天气聚类，选取适宜位置的分位点，搭建了双向长短期记忆神经网络（bidirectional long short term memory，BiLSTM）挖掘双向时序特征。然后，引入自注意力机制（self-attention， SA）动态聚焦序列关键信息，并调用粒子群算法进行神经网络参数寻优，将最优参数置入BiLSTM-SA优化模型进行点预测。最后，基于点预测结果进行误差分析，利用分位数回归（quantile regression，QR）构建了QR-BiLSTM-SA概率预测模型。算例结果表明，所提方法在光伏出力超短期概率预测中精度可达95%以上，并具有良好的泛化能力，可为新型配电系统运行调度提供可靠依据。

Accurate ultra-short-term prediction of photovoltaic （PV） power is pivotal in mitigating the adverse effects of PV output uncertainty on power systems, thereby furnishing a dependable foundation for grid decision-making and scheduling. Therefore, a method for ultra-short-term prediction of photovoltaic output based on deep learning technology is proposed. Firstly, leveraging historical PV data, feature correlation analysis and K-means++ weather clustering are performed to discern pertinent patterns. And employing strategically positioned quantiles, a novel bidirectional long short term memory （BiLSTM） neural network is then constructed to capture bidirectional temporal dependencies. Then, a self-attention mechanism （SA） is introduced to dynamically emphasize key sequential information and a particle swarm optimization algorithm is integrated to optimize the parameters of the neural network. Optimal parameters are then assimilated into the BiLSTM-SA optimization framework for point prediction. Finally, through meticulous error analysis, a quantile regression （QR） is developed to delineate QR-BiLSTM-SA probability prediction model. The results show that the proposed method achieves accuracy exceeding 95% in ultra-short-term PV output probability prediction and has a good generalization ability, offering a robust foundation for the operation and scheduling of modern power systems.