光照的间歇性使光伏发电功率波动性较大，导致光伏发电功率的预测准确率较低。为此，提出一种基于连续变分模态分解(successive variational mode decomposition，SVMD)、贝叶斯优化(Bayesian optimization，BO)算法和双向时序卷积网络(bidirectional temporal convolutional network, BiTCN)的超短期光伏发电功率预测模型，以提高预测精度。首先，通过SVMD将原始光伏发电功率分解为多个功率分量和功率残差，以获得多个波动性小的序列；然后，使用改进的BiTCN代替单向时序卷积网络(temporal convolutional network，TCN)，完成低耗时下SVMD分解结果的双向特征提取与预测；之后，使用BO算法高效寻找BiTCN超参数，从而提高BiTCN对各功率分量和功率残差的预测精度；最后，求和并重构预测结果，实现超短期光伏发电功率预测。实验证明，该模型与单一的TCN模型相比，均方根误差(root mean square error，RMSE)减小了35.18%，决定系数提升了4.82%。

Intermittent sunlight leads to significant fluctuations in photovoltaic power generation, resulting in low accuracy in predicting the power output. In this study, a new ultra-short-term photovoltaic power generation forecasting model based on successive variational mode decomposition (SVMD), Bayesian optimization (BO) algorithm, and bidirectional temporal convolutional network (BiTCN) is proposed to improve prediction accuracy. Initially, the raw photovoltaic power generation data is decomposed into multiple power components and a power residual using SVMD to obtain multiple sequences with small fluctuations. Subsequently, the improved BiTCN replaces the temporal convolutional network (TCN) to perform bidirectional feature extraction and prediction of the SVMD decomposition results with low latency. Then, BO algorithm is used to search for BiTCN hyperparameters efficiently, so as to improve the prediction accuracy of each power component and power residual. Finally, the predicted results are summed and reconstructed to achieve ultra-short-term photovoltaic power generation prediction. Experiments demonstrate that the proposed model achieves a 35.18% reduction in root mean square error (RMSE) and a 4.82% increase in coefficient of determination compared to the single TCN model.