基于遗传算法优化小波神经网络的短期天然气负荷预测

天然气负荷预测对于燃气经营企业尤其重要，对保证天然气管网的用气量、优化管网的调度具有重要意义.传统的天然气预测模型预测精度低、模型泛化程度低.为了克服模型缺陷，提出了一种基于遗传算法优化小波神经网络的天然气负荷预测模型.通过遗传算法对小波神经网络的阈值以及网络连接权值等参数进行优化，从而建立预测效果最好的模型，通过企业提供的历史门站数据对预测模型进行验证.仿真结果表明，使用遗传算法优化网络参数的小波神经网络提高了模型的预测精度，具有一定的工程应用价值.

Short-Term Natural Gas Load Forecasting Based on Wavelet Neural Network Optimized by Genetic Algorithm

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.