【摘要】 提出一种基于模态相关性和重构误差的改进变分模态分解（VMD）算法以及改进减法平均算法（CSABO）优化时间卷积网络（TCN）和双向门控循环单元（BiGRU）组成的中短期光伏功率预测模型。首先，利用改进VMD将历史光伏数据分解为多个不同频率的分量；然后，将各分量与关键气象因素结合，通过TCN-BiGRU模型对各时序数据进行预测，重构得到光伏功率预测值；最后，使用CSABO对预测模型的参数寻优，提高模型性能。以澳大利亚实际光伏数据为算例进行实验分析，结果表明所提模型与EMD-TCN-BiGRU、CEEMDAN-TCN-BiGRU和VMD-CNN-LSTM模型相比，各项评价指标均最优，具有更高的预测精度。更多还原

【Abstract】 An improved variational mode decomposition(VMD) algorithm based on modal correlation and reconstruction error, and an improved subtraction-average-based optimizer(CSABO) to optimize short-and medium-term photovoltaic(PV) power prediction model consisting of temporal convolutional network(TCN) and bidirectional gated recurrent unit(BiGRU) are proposed. Firstly, the historical PV data are decomposed into multiple components with different frequencies using the improved VMD. Then, the components are combined with key meteorological factors, and the PV power forecasts are reconstructed by the TCN-BiGRU model by forecasting each time series data separately. Finally, the parameters of the prediction model are optimized using CSABO to improve the model performance. The actual Australian PV data is used as an arithmetic example for experimental analysis, and the results demonstrate that the proposed model exhibits the best evaluation indexes and higher prediction accuracy compared with EMD-TCN-BiGRU, CEEMDAN-TCN-BiGRU and VMD-CNN-LSTM models.更多还原