基于VMD-LSTM-IPSO-GRU的电力负荷预测

为了挖掘电力负荷数据中的潜藏信息,提高负荷预测的精度,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)、优化长短期神经网络(long-term and short-term memory network,LSTM)、改进的粒子群算法(improve particle swarm optimization,IPSO)优化门控循环单元(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,更加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,相对于其它常规模型,该混合模型可有效的提取模态特征,具有更高的预测精度。

Short-term Load Forecasting Based On VMD-LSTM-IPSO-GRU

To explore the hidden information in power load data and improve the accuracy of 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) is proposed to deal with the strong nonlinearity and non-stationarity of power load. First, use correlation analysis to determine the input factors, then use the VMD algorithm to decompose the load data into a series of intrinsic mode functions (IMF) and residual quantities combined with sample entropy decomposition to more reasonably determine the decomposition layer number and penalty factor. Secondly, according to the zero-crossing rate, these quantities are divided into low-frequency and high-frequency components. The low-frequency component uses the LSTM network, and the high-frequency component uses the IPSO-GRU network for prediction respectively. Finally, the predicted results are reconstructed to obtain the final result of power load.SThe simulation results show that compared with other conventional models, this hybrid model can effectively extract modal features and has higher prediction accuracy.