Integration of Machine Learning-Based Demand Forecasting and Economic Optimization for the Natural Gas Supply Chain

Abstract

This paper proposes a profit optimization method for the natural gas industry chain driven by demand forecasting. The method mainly consists of two core components: the construction of a natural gas demand forecasting model and the solution of an industry chain profit optimization model. In the forecasting stage, three models are trained using historical natural gas demand data, and the optimal model is selected based on performance evaluation indicators to predict natural gas demand for the coming month. In the optimization stage, the physical and operational characteristics of key components in the natural gas pipeline network are fully considered, and a nonlinear programming model is formulated with the objective of maximizing the overall profit of the industry chain. The model is validated using historical data. Finally, the demand forecast results are incorporated into the optimization model to calculate the expected industry chain profit for the next month. The findings of this study can provide theoretical foundations and quantitative decision-making support for natural gas suppliers to develop more economically efficient gas supply strategies.