AI for Smart Meter Fraud & Anomaly Detection |…

AI for Smart Meter Fraud & Anomaly Detection

Detect fraud and anomalies in smart meter data using consumption patterns, topology, and device signals to cut non-technical losses.

Non-technical losses erode utility revenue. AI detects theft, tampering, and faulty meters by analyzing usage, topology, and device events, prioritizing investigations with evidence.

Voltage/power quality anomalies

Investigation Prioritization: This system prioritizes investigations based on risk scores assigned to accounts, feeders, or areas.

Risk scoring of accounts/feeders/areas: Risk is calculated using a combination of factors like unusual consumption patterns and device signals.

live demo · related simulation● LIVE

MDM/AMI data; events; topology/GIS mapping

Weather/tariff and peer segmentation: The system segments customers based on weather conditions, tariff plans, and comparisons with similar customer groups.

Investigation outcomes for labeling: Identified anomalies are labeled to facilitate further investigation and analysis.

Frequently asked questions

What is the primary goal of using AI in smart meter data analysis?

The primary goal is to detect fraud and anomalies within smart meter data, ultimately reducing non-technical losses and protecting utility revenue.

How does the system prioritize investigations?

The system prioritizes investigations by assigning risk scores to accounts, feeders, or areas based on unusual consumption patterns and device signals.

What types of data are analyzed to identify anomalies?

The system analyzes consumption patterns, topology (the network connections between meters), and device signals to detect deviations from normal behavior.

How can the results of investigations be used to improve the AI model?

Feedback from investigations is used to iterate on the AI model, expanding its coverage to more regions and tariffs.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation