配电网数字孪生技术是当前电力系统和信息技术融合发展的重要产物，它通过构建实体配电网的虚拟模型，在数字空间中模拟配电网的物理行为和运行状态，实现对实体配电网的仿真分析。由于配电网涉及的系统多样、状态复杂，现有配电网数字孪生仿真平台技术仍有待提升。提出了一种基于小波与长短期记忆（long short-term memory, LSTM）网络融合的数字孪生状态预测方法，该方法在现有小波变换以及LSTM神经网络的基础上，构造了面向电力状态以及天气因素的小波-LSTM融合模型，借助离散小波变换将高维输入数据转化为细节与轮廓系数，然后通过LSTM神经网络对数据处理求解以及结果的融合，从而形成准确的预测结果。在真实数据集上进行验证，表明小波-LSTM融合模型较现有LSTM网络在平均绝对百分比误差（mean absolute percentage error, MAPE）指标上有显著提升。最后，还在不同行业的数据集上进行了测试，结果表明小波LSTM预测方法可适用于不同行业的状态数据，相较于小波Lasso、LSTM、STL-LSTM在MAPE方面具有更好的性能，可为未来数字孪生的状态预测提供良好的支持。

The digital twin technology of the distribution network is an important product resulting from the integration and development of the power system and information technology. The technology simulates the physical behavior and operational status of the distribution network in a digital space by constructing a virtual model of the physical distribution network, enabling comprehensive simulation and analysis. Due to the diverse systems and complex states involved, the existing digital twin simulation platform technology for distribution networks still requires improvement. A wavelet-LSTM fusion model for power state and weather factors was constructed based on the existing wavelet transform and long short-term memory (LSTM) neural network. The high-dimensional input data were converted into detail and contour coefficients using discrete wavelet transform. Subsequently, LSTM neural networks were constructed to process the data and fuse the results, thereby forming accurate prediction outcomes. This method was validated on real datasets, showing that the wavelet-LSTM fusion model significantly improves the mean absolute percentage error (MAPE) compared to the existing LSTM network. Additionally, the method was tested on datasets from different industries. Compared to wavelet-Lasso, LSTM, and STL-LSTM, it exhibits better performance in terms of MAPE, demonstrating that the wavelet LSTM prediction method can be applied to state data from various sectors, thereby providing robust support for future state prediction of digital twins.