Abstract:Natural gas load forecasting is especially important for gas-operated enterprises. It is extremely important to ensure the gas consumption of natural gas pipeline network. The traditional natural gas prediction model has low prediction accuracy and low generalization of the model, so that accurate load prediction cannot be performed. In order to overcome these defects, a natural gas load forecasting model based on wavelet neural network optimized by genetic algorithm is proposed. The genetic algorithm is used to optimize the parameters of wavelet neural network threshold and network connection weight to establish the best prediction model. The validity, feasibility, and accuracy of the prediction model are verified by the historical gate data provided by the enterprise. The simulation results show that the wavelet neural network using genetic algorithm to optimize the network parameters improves the prediction accuracy of the model and has sound engineering application value.