Data Empowerment Evaluation: A Revenue Estimation Model for Scenario-Based Applications in Gas Metering

This paper introduces a framework for scenario-based data revenue estimation, leveraging big data from a gas company's metering devices. The proposed methodology combines data analysis algorithms and machine learning models to detect metering device faults, reduce gas loss, improve operational efficiency, and lower labor costs. By utilizing smart meter data, the framework enables comprehensive monitoring through automated analysis, reducing the need for manual intervention. Machine learning models further enhance the system by predicting device anomalies early, allowing for proactive maintenance and optimization of operations. This paper emphasizes the practical application of this framework, addressing potential challenges in system integration, such as data quality management and algorithm optimization, offering a scalable approach for enhancing business decision-making and operational performance.