Hyperparameters for each comparison model.

The access of new energy improves the flexibility of distribution network operation, but also leads to more complex mechanism of line loss. Therefore, starting from the nonlinear, fluctuating and multi-scale characteristics of line loss data, and based on the idea of decomposition prediction, this paper proposes a new method of line loss frequency division prediction based on wavelet transform and BIGRU-LSTM (Bidirectional Gated Recurrent Unit-Long Short Term Memory Network).Firstly, the grey relation analysis and the improved NARMA (Nonlinear Autoregressive Moving Average) correlation analysis method are used to extract the non-temporal and temporal influencing factors of line loss, and the corresponding feature data set is constructed. Then, the historical line loss data is decomposed into physical signals of different frequency bands by using wavelet transform, and the multi-dimensional input data of the prediction network is formed with the above characteristic data set. Finally, the BIGRU-LSTM prediction network is built to realize the probabilistic prediction of high-frequency and low-frequency components of line loss. The effectiveness and applicability of the method proposed in this paper were verified through numerical simulation. By dividing the line loss data into different frequency bands for frequency prediction, the mapping relationship between different line loss components and influencing factors was accurately matched, thereby improving the prediction accuracy.

新能源并网提升了配电网运行的灵活性，但也使得线损机理愈发复杂。为此，本文从线损数据的非线性、波动性与多尺度特征出发，结合分解预测思想，提出了一种基于小波变换（wavelet transform）与BIGRU-LSTM（双向门控循环单元-长短期记忆网络，Bidirectional Gated Recurrent Unit-Long Short Term Memory Network）的线损分频预测新方法。首先，采用灰色关联分析与改进型NARMA（非线性自回归移动平均，Nonlinear Autoregressive Moving Average）相关性分析方法，提取线损的非时序与时序影响因素，并构建对应的特征数据集。随后，通过小波变换将历史线损数据分解为不同频段的物理信号，并与上述特征数据集共同构成预测网络的多维输入数据。最后，构建BIGRU-LSTM预测网络，实现线损高低频分量的概率化预测。通过数值仿真验证了本文所提方法的有效性与适用性。通过将线损数据划分为不同频段进行分频预测，可精准匹配不同线损分量与影响因素间的映射关系，进而提升预测精度。