为了挖掘电力负荷数据中的潜藏信息，提高短期负荷预测的精度，针对电力负荷强非线性、非平稳性等特点，提出一种基于变分模态分解（variational mode decomposition, VMD）、长短时记忆神经网络（long-term and short-term memory network, LSTM）、改进的粒子群算法（improve particle swarm optimization, IPSO）和门控循环单元（gated recurrent unit neural network, GRU）的混合预测模型。首先，使用相关性分析确定输入因素，再将负荷数据运用VMD算法结合样本熵分解为一系列本征模态分量（intrinsic mode fuction, IMF）和残差量，进而合理地确定分解层数和惩罚因子；其次，根据过零率将这些量划分为低频和高频，低频分量使用LSTM网络，高频分量利用IPSO-GRU网络分别进行预测；最后，将预测结果重构得到电力负荷的最终结果。仿真结果表明：相对于其他模型，所提混合模型可有效的提取模态特征，具有更高的预测精度。

To explore the hidden information in power load data and improve the accuracy of short-term load forecasting, a hybrid prediction model based on variational mode decomposition (VMD), long-term and short-term memory network (LSTM), improved particle swarm optimization (IPSO) algorithm and gated recurrent unit neural network (GRU) was proposed with consideration of strong non-linearity and non-stationarity in power load. First, correlation analysis was employed to determine input factors. The VMD algorithm combined with sample entropy was employed to decompose the load data into a series of intrinsic mode function (IMF) and residual components, which then allowed for the rational determination of the decomposition levels and penalty factors. Then, these quantities were divided into low-frequency and high-frequency components based on the zero-crossing rate. The low-frequency components were forecasted using an LSTM network, while the high-frequency components were predicted with an IPSO-GRU network. Finally, the predicted results were reconstructed to obtain the final result of power load. Simulation results show that the proposed hybrid prediction model can effectively extract modal features and possesses higher predictive accuracy compared with alternative models.