随着光伏电站装机容量的不断增加以及电力体制改革和新型能源体系的推进，光伏出力预测面临更高的精度要求。提出了一种融合最大信息系数（maximum information coefficient, MIC）、自然随机优化（natural randomized optimization, NRBO）、有限模态分解（finite modal decomposition, FMD）和双向注意力机制下的门控制循环单元（bidirectional gated recurrent unit-attention, BiGRU-Attention）的光伏电站出力模型。在运用MIC方法确定影响光伏电站出力关键气象因素的基础上，基于NRBO和FMD技术，对光伏电站出力数据进行模态分解，生成模态分量，避免高频噪声数据对训练的结果产生影响；然后，在GRU（gated recurrent unit）模型中引入双向注意力机制，增强模型对复杂时序数据的学习能力，最终实现了光伏电站出力的精准预测。实验结果表明，对比其他模型，所提模型预测精度显著提升。

As the installed capacity of photovoltaic power stations continues to grow, and with the advancement of power system reforms and new energy systems, the accuracy requirements for photovoltaic output forecasting have become increasingly stringent. A photovoltaic power station output model integrating MIC (maximum information coefficient), NRBO (natural randomized optimization), FMD (finite modal decomposition), and BiGRU (bidirectional gated recurrent unit) with an attention mechanism was proposed. The MIC method was employed to identify key meteorological factors affecting photovoltaic power output. Furthermore, NRBO and FMD techniques were innovatively applied to decompose photovoltaic output data into modal components, which helped eliminate high-frequency noise that could affect the training results. A bidirectional attention mechanism was then introduced into GRU model to enhance its ability to learn from complex time-series data, ultimately leading to more accurate forecasting of photovoltaic power station output. Experimental results demonstrate that the proposed model significantly outperforms other models in terms of forecasting accuracy.