基于CEEMD-GRU模型的短期电力负荷预测方法

针对电力负荷序列不平稳、随机性强,直接输入模型会导致拟合效果差、预测精度低等问题,本文提出了一种基于添加互补白噪声的互补集合经验模态分解(complementary ensemble empirical mode decomposition, CEEMD)以及门控循环单元神经网络(gated recurrent unit neural network, GRU)融合的预测方法。首先,针对传统经验模态分解(empirical mode decomposition, EMD)分解方法处理干扰信号大的序列时,存在的模态混叠问题,提出了CEEMD分解方法,加入互补白噪声,将原始序列分解成不同尺度的子序列,随后使用GRU神经网络,并优化网络超参数,从而获得最好的预测结果。通过实验证明,该方法重构误差小,预测效果好。

Short-term Load Forecasting based on Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit Neural Network

For power load sequence is not smooth, strong randomness, input model directly will lead to poor fitting effect and low prediction accuracy, this paper proposes a add complementary white noise based on complementary ensemble empirical mode decomposition (CEEMD) and gated recurrent unit neural network (GRU) combination forecast method of power load short-term prediction precision is improved effectively. First of all, for the empirical mode decomposition (EMD) to deal with the sequence of large interference signals there is a modal aliasing problem, proposed CEEMD decomposition method, the addition of complementary white noise, the original sequence into different scales of the sub-sequence, then use GRU neural network, and optimize the network super parameters, so as to get the best prediction results. The experiment results show that the reconstruction error is small and the prediction effect is good.