C3 IoT > Case Studies > Largest Production Deployment of AI and IoT Applications

Largest Production Deployment of AI and IoT Applications

Technology Category

Analytics & Modeling - Machine Learning
Analytics & Modeling - Predictive Analytics
Analytics & Modeling - Real Time Analytics
Application Infrastructure & Middleware - Data Exchange & Integration
Functional Applications - Remote Monitoring & Control Systems
Sensors - Environmental Sensors
Sensors - Utility Meters

Applicable Industries

Electrical Grids

Applicable Functions

Business Operation
Maintenance

Use Cases

Fraud Detection
Predictive Maintenance

The Customer

Enel

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About The Customer

- 96GW Generation Capacity- €71 Billion Annual Revenue- 62M Customers Globally- 73,000 Employees

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The Challenge

To increase efficiency, develop new services, and spread a digital culture across the organization, Enel is executing an enterprise-wide digitalization strategy. Central to achieving the Fortune 100 company’s goals is the large-scale deployment of the C3 AI Suite and applications. Enel operates the world’s largest enterprise IoT system with 20 million smart meters across Italy and Spain.

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The Solution

The teams worked together to replace traditional non-technical loss identification processes with the C3 Fraud Detection application. The new application uses advanced AI capabilities to prioritize potential cases of non-technical loss at service points, based on a blend of the magnitude of energy recovery and likelihood of fraud.

The system integrates and correlates 10 trillion rows of data from seven Enel source systems and 22 data integrations into a unified, federated cloud image in near real-time, running on Amazon Web Services. Using analytics and more than 500 advanced machine learning features, C3 Fraud Detection continuously updates probability of fraud for each customer meter.

To improve grid reliability and reduce the occurrence of faults, Enel deployed the C3 Predictive Maintenance application for 5 control centers. The application uses AI to analyze real-time network sensor data, smart meter data, asset maintenance records, and weather data to predict feeder failure.

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Data Collected

Electricity Meters, Weather

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Operational Impact

[Process Optimization - Predictive Maintenance]
The application uses AI to analyze real-time network sensor data, smart meter data, asset maintenance records, and weather data to predict feeder failure.

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Quantitative Benefit

2X Performance Increase in Identifying Unbilled Energy
€2.5B Cumulative Economic Benefit Target from Digitalization by 2021

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