针对阵列间距较小的光伏系统存在相邻阵列间阴影遮挡及视域遮蔽现象，导致光伏系统入射斜面总辐照度及输出功率减小、短期光伏功率预测精度低等问题，本文基于太阳辐射传输模式及几何阴影遮挡原理提出了一种相邻阵列遮挡影响下的短期光伏功率预测方法。首先，对大气透射率聚类进行天气类型划分，根据不同天气类型考虑遮挡情况差异。其次，结合太阳几何光学刻画的后排阵列与地面阴影动态变化及视域因子实时修正各天气类型下斜面总辐照度，进而构建遮挡下斜面总辐照度模型。再次，通过相关性分析验证了遮挡下斜面总辐照度与光伏功率间具有更强的相关性。最后，以遮挡下斜面总辐照度作为原理预报法及卷积-双向长短期记忆(CNN-BiLSTM)网络的输入特征，对不同天气类型进行预测试验和误差分析，结果表明所提方法在不同天气类型下均有效提升了短期光伏功率预测精度。

Aiming at the problems of reduced total irradiance on tilted surfaces and output power of photovoltaic (PV) systems caused by shading and sky view masking of adjacent arrays, also leading to low accuracy for short-term PV power prediction, this paper proposes a short-term photovoltaic power prediction method considering shading and sky view masking for solar radiation transposition models based on solar geometry. First, classify weather types by clustering atmospheric transmissivity, and account for differences in shading conditions according to the various weather types. Then, the total irradiance on inclined surface under shading is corrected in real time for each weather type by combining the dynamic shadow changes on the second row and ground-space and the view factors, and thereby a new total irradiance model under shading conditions is presented. Moreover, the correlation analysis illustrates that PV power has stronger correlation with the inclined irradiance under shading. Finally, the inclined irradiance under shading is used as input feature of the principle prediction method and convolutional neural network - bidirectional long short-term memory （CNN-BiLSTM）for PV prediction test under different weather types. The results show that the proposed method effectively improves the performance of short-term PV power prediction under different weather types.