基于VMD-CNN-BIGRU的电力系统短期负荷预测

Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU

摘要: 为提高负荷预测精度，考虑了历史负荷本身内在规律及外部影响因素，提出一种基于变分模态分解（variational modal decomposition，VMD） –卷积神经网络（convolutional neural networks，CNN） –双向门控循环单元（bi-directional gated recurrent unit，BIGRU）混合网络的短期负荷预测方法，改善了训练时长和预测效果。通过仿真分析验证了所提方法的有效性，且该方法与其他模型相比有更高的负荷预测精度和更强的鲁棒性，能够提高电力系统短期负荷预测的精确度。

Abstract: In order to improve the accuracy of load prediction, taking into account the internal laws and external influencing factors of historical load itself, a kind of variational modal decomposition (VMD) -convolutional neural networks (CNN) -bi-directional gated recurrent units are proposed. BIGRU) short-term load prediction method for hybrid networks, improving training duration and prediction results. The effectiveness of the proposed method is verified by simulation analysis, and the method has higher load prediction accuracy and stronger robustness than other models, which can improve the accuracy of short-term load prediction of power system.