基于改进LMD与GRU网络的短期燃气负荷预测

Short-Term Gas Load Forecasting Based on WT-LMD and GRU Neural Network

针对燃气负荷数据非线性、非平稳性的特点,本文提出一种基于改进的LMD算法与GRU神经网络的组合预测模型.模型首先利用改进后的LMD算法对燃气负荷数据进行序列分解,改进的LMD方法采用分段牛顿插值法代替传统的滑动平均值法来获得局部均值函数和包络估计函数,改善了传统LMD方法存在的过平滑问题.之后,再将得到的若干PF分量进行小波阈值去噪处理,获得有效的分量数据.最后,利用GRU神经网络分别预测各分量值,将它们相加得到最终的负荷预测值.仿真实验表明,提出的方法与单个GRU神经网络以及结合传统LMD算法的GRU网络相比,预测精度更高.

In view of the nonlinearity and non-stationarity of gas load data, this paper presents a combined forecasting model based on improved WT-LMD and GRU neural networks. Firstly, the model decomposes the gas load data by using the improved LMD algorithm, which improves the over-smoothing problem of that the traditional LMD method uses piecewise Newton interpolation instead of the traditional sliding average method to obtain local mean function and envelope estimation function. After that, the PF components are processed with wavelet threshold denoising to obtain effective component data. Finally, The GRU neural network is used to predict the value of each component separately, and the final predicted value of load is obtained by adding them. Simulation results show that the proposed method is more accurate than single GRU neural network and GRU network combined with traditional LMD algorithm.