Virtualization of Metering Systems with AI & Physics-Based Models

Accurate metering across complex infrastructures, such as pipelines, substations, or industrial facilities -is critical for efficient operation, system optimization, and regulatory compliance. However, deploying high-precision physical meters at every segment is often cost-prohibitive, hard to maintain, and technically infeasible in remote or extreme environments.

Many systems operate with incomplete or inconsistent metering coverage, leading to blind spots in flow, consumption, or system performance data. Environmental constraints, changing operating conditions, and aging hardware further limit the accuracy and flexibility of conventional instrumentation.

We address these challenges by building AI-augmented metering layers that combine machine learning models, historical metering data collected under verified conditions, and real-time sensor feeds (e.g., pressure, temperature, and flow rate proxies). These systems deliver scalable, adaptive estimations across diverse infrastructure, supplementing or replacing physical meters where appropriate.

Basic featureset: Cloud-hosted estimation models integrated into metering infrastructure Real-time performance monitoring based on sensor data Alerting mechanisms for data anomalies or instrumentation failure

Advanced featureset: Physics-informed AI models for high accuracy under dynamic conditions System-wide estimations across hierarchical infrastructure (e.g., feeder → substation → grid) Continuous learning workflows aligned with operational and environmental changes

Solution architecture:

The architecture builds a virtualized metering layer across oil, gas, or energy systems. Real-time sensor data is ingested via AWS IoT SiteWise and stored in Amazon Timestream. Machine learning models, developed in Amazon SageMaker and trained on historical metering data collected under verified conditions, estimate flow rates, energy transfer, or equipment performance. Predictions are streamed to Amazon QuickSight dashboards or operational systems. AWS Lambda and Step Functions enable automation, monitoring, and periodic retraining.

This design supports:

Scalable metering across infrastructure without proportional hardware cost Resilience and adaptability in harsh or data-sparse environments High accuracy and system visibility, even in unmetered or under-instrumented areas

Solutions are tailored to your specific requirements and eligibility. Get a free consultation with our product managers and engineers to kick-start your project—contact us at hello@xenoss.io  or request a private offer.