Short Term Gas Load Prediction Based on CNN-LSTM Hybrid Model

Load prediction play a significant role in the field of gas development. In order to effectively mine historical data information and improve the accuracy of short-term gas load, this paper focuses on the gas load based on the time series nonlinear characteristics. A Convolutional Neural Networks (CNN) and Long Short-Term Memory Network (LSTM) are combined to predict the short term load of gas with the advantages of the two types of network. Firstly, CNN is used to extract the important local spatial features of gas load information with 2 layers convolutional network, Then, the latent features of gas load extracted by CNN are feed into the LSTM, which can maintain a good memory of the time series with a long time span, and can fully consider the time correlation of data. Finally, the prediction value of the mixed model is generated according to the deep features of the training data. The simulation result investigates that compared with the CNN, LSTM, BiLSTM and Seq2Seq neural network model, the CNN-LSTM network model performs better to extract the spatiotemporal feature, which can predict short term gas load more accurately.