基于贝叶斯优化的长短期记忆网络模型的区域天然气需求预测模型OACHSSCD

To improve the accuracy of regional natural gas demand forecasting,this paper proposes a medium-and long-term forecasting model based on a Bayesian optimization-enhanced Long Short-Term Memory(LSTM)network.It introduces a hybrid screening mechanism combining Pearson correlation analysis and random forest,conducts different provinces nationwide clustering with feature similarity based on strongly correlated feature indicators,and builds LSTM model using Bayesian optimization-enhanced.This process includes six sequential stages:collection of feature time series influencing natural gas demand,data preprocessing,region clustering based on feature similarity,selection of feature extraction methods,model construction,and error analysis of forecasting results.Test results show that the annual forecast errors are within 15%for most regions and within 5%for the others,demonstrating the model's reliability.The proposed model offers such three main advantages as overcoming the blindness inherent in traditional LSTM parameter tuning and enhancing the model's nonlinear fitting capability and medium-to long-term prediction performance,good suitability for demand forecasting scenarios across different provinces nationwide based on administrative divisions,and achieving medium-and long-term regional natural gas demand forecasting to meet the planning needs of natural gas suppliers and pipeline network operators.