Natural Gas Flow Metering Error Compensation Model Based on XGBoost-Stacking Ensemble Learning

Abstract

With the rapid growth of natural gas consumption in my country, flow metering accuracy is crucial for energy trade settlement and transmission and distribution loss control. However, metering errors caused by multiple factors, such as liquefied natural gas vaporization, metering equipment failures, and improper parameter settings, pose challenges to traditional compensation methods that rely on simple mathematical models. This study aims to construct a high-precision natural gas flow metering error compensation model to intelligently improve metering accuracy and reduce economic losses caused by errors. Based on multi-source data from the natural gas pipeline network, after data cleaning and standardization preprocessing, this study combines the advantages of XGBoost in handling nonlinear relationships with the Stacking ensemble strategy, supplemented by Bayesian optimization and random search for parameter tuning. Ultimately, an XGBoost-Stacking ensemble learning error compensation model is constructed. Experimental results show that the proposed model achieves a mean absolute error of 0.0298, a root mean square error of 0.0451, and a coefficient of determination of 0.9432, significantly outperforming traditional regression models and single XGBoost models. The compensation accuracy meets the industry requirement of ≤0.5%, and generalization capability is stable. This model provides an effective technical path for resolving natural gas metering errors, reducing transmission losses, ensuring trade fairness, and promoting the intelligent development of the natural gas industry.