基于EMD-CNN-LSTM混合模型的短期电力负荷预测

Short-term Power Load Forecasting Method Based on EMD-CNN-LSTM Hybrid Model

为了更有效地提取电力负荷数据中的潜藏特征与隐藏信息,提高电力负荷预测精度,针对负荷具有较强非线性、非平稳性和时序性特点,提出一种基于经验模态分解(empirical mode decomposition,EMD)、卷积神经网络(convolutional neural network,CNN)和长短期记忆网络(long-term and short-term memory network,LSTM)的混合模型短期电力负荷预测方法,将海量过往负荷数据、温度和历史电价信息以滑动窗口方式构造串联特征向量作为输入,先利用EMD将数据重构成多个分量,将高、中和低频分量各自叠加组合,再运用CNN提取高、中分量的潜藏特征,减少权值数量,并以特征向量的方式输入LSTM网络进行负荷预测,最后叠加各分量预测结果得到最终负荷预测值。实验结果表明,相对于BP神经网络(Back Propagation Neural Network)、支持向量机(support vector machine,SVM)、LSTM模型和EMD-LSTM模型,此模型具有更高的负荷预测精度。

To improve the accuracy of power load forecasting,it is necessary to more effectively extract the hidden features and information in the massive data of power load.Based on the fact that the load has the characteristics of strong nonlinear,non-stationary and temporal,we proposed a short-term power load forecasting method based on the hybrid model of empirical mode decomposition(EMD),convolutional neural network(CNN)and long-term and short-term memory network(LSTM).In the process of forecasting,the massive past load data,temperature and the historical information of electricity price are constructed as a series feature vector and taken as input with time sliding window.First,we used EMD to reconstruct the data into multiple components,and superimposed and combined the high,medium and low frequency components.Then we used CNN to extract the hidden features of the high and medium components to reduce the weight.The components were used as input data for the LSTM network in the form of feature vectors for load prediction.Finally,the prediction results of each component were superimposed to obtain the final value of load prediction.The experimental results show that this model has higher load forecasting accuracy than BP neural network(Back Propagation Neural Network),support vector machine(SVM),long-term and short-term memory network and EMD-LSTM models.