Automation Workflow for Advanced Metering Infrastructure (AMI) Data Analytics

Automated anomaly detection models process millions of daily meter reads to flag irregularities—such as stopped meters, reverse flows, or consumption patterns indicative of theft—within hours, not weeks. This directly recovers lost revenue and reduces the cost of manual field investigations. The workflow integrates ML inference with SCADA and customer information systems (CIS) to route validated alerts to revenue protection teams with supporting evidence.

Instead of relying on outdated load shapes, the pipeline continuously segments customers and extracts precise, localized load profiles. This provides distribution planners with current, granular data on transformer loading, peak demand drivers, and hosting capacity for new solar interconnections. Automated feature engineering and clustering models feed directly into planning tools like CYME or Synergi, reducing manual data preparation from weeks to days.

Automated customer segmentation identifies ideal candidates for time-of-use rates, demand response, or energy efficiency programs based on actual consumption behavior and propensity models. This increases program enrollment rates and effectiveness by moving beyond demographic proxies. The workflow ingests AMI data, enriches it with property attributes, and outputs targeted lists for marketing systems, improving marketing spend ROI and accelerating grid flexibility acquisition.

The architecture intelligently routes detected anomalies—whether for meter health, premise outages, or power quality issues—to the correct utility department (e.g., Metering, Distribution Operations, Customer Service). This eliminates manual dispatch and cross-departmental email chains. By integrating with workforce management and outage management systems (OMS), it ensures the right crew with the right information is deployed, reducing truck rolls and mean-time-to-restore.

Automated data validation and audit trails ensure AMI data used for regulatory filings (e.g., reliability indices, energy efficiency claims) is accurate and traceable. The pipeline flags data gaps and imputes values using approved methods, creating a consistent, defensible record. This reduces the labor-intensive, quarterly scramble for data reconciliation and lowers the risk of compliance penalties or rate case challenges.

Predictive models automatically correlate meter data with transformer age, environmental conditions, and failure histories to generate asset health scores. This enables condition-based maintenance, preventing costly failures and extending asset life. The workflow integrates with enterprise asset management (EAM) systems like SAP or IBM Maximo to automatically generate work orders for high-risk assets, shifting from reactive to predictive operations.

The workflow ingests raw 15-minute interval data from millions of smart meters via head-end systems (e.g., Itron, Landis+Gyr) and validates for gaps, duplicates, and implausible values. This automated validation layer ensures data quality before downstream processing, preventing 'garbage-in, garbage-out' scenarios that corrupt ML models and business insights. Integration with GIS and customer information systems (CIS) enriches meter points with location and customer attributes.

An automated feature engineering pipeline calculates key metrics: load factor, daily/weekly profiles, volatility, and non-coincident peaks. These features feed unsupervised ML models (Isolation Forest, Autoencoders) to flag anomalies indicative of meter faults, tampering, or theft. The system clusters customers by consumption behavior for targeted demand-side management programs. Rules-based agents escalate high-confidence theft alerts directly to revenue protection teams with supporting evidence.

Trained models run batch and streaming inference on the enriched dataset. A central orchestrator (e.g., Apache Airflow, Prefect) manages the pipeline, routing outputs: anomaly alerts to field service management systems, customer segments to marketing automation platforms, and transformer loading forecasts to grid planning teams. The architecture uses a feature store to ensure consistency between training and inference, and a model registry to manage versions and performance drift.

Not all automated decisions are fully autonomous. High-stakes alerts (e.g., potential major theft, critical meter failure) are routed through a human review queue in a case management system like ServiceNow. Reviewers see model confidence scores, supporting data visualizations, and historical context. Their feedback closes the loop, retraining models and refining rules. This controlled escalation preserves operational trust and meets regulatory requirements for revenue-critical actions.

The entire pipeline is instrumented for observability: data lineage, pipeline health, model performance (precision/recall drift), and business KPIs. A dashboard tracks operational ROI: revenue recovered from theft detection, reduction in meter-related service calls, and improved hit rates for energy efficiency programs. This connects technical performance directly to utility P&L impact, justifying the automation investment and guiding continuous improvement.