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

	Largest Production Deployment of AI and IoT Applications C3 IoT

Largest Production Deployment of AI and IoT Applications

C3 IoT

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
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. Read More
About Customer	- 96GW Generation Capacity- €71 Billion Annual Revenue- 62M Customers Globally- 73,000 Employees Read More
Customer Name	Enel Read More
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. Read More Log in to view content
Data Collected	Electricity Meters, Weather Read More Log in to view content
Contents

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

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.

About Customer

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

Customer Name

Enel

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.

Data Collected

Electricity Meters, Weather

Impact #1	[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.

Impact #1

[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.

Benefit #1	2X Performance Increase in Identifying Unbilled Energy
Benefit #2	€2.5B Cumulative Economic Benefit Target from Digitalization by 2021

Benefit #1

2X Performance Increase in Identifying Unbilled Energy

Benefit #2

€2.5B Cumulative Economic Benefit Target from Digitalization by 2021

Download PDF Version

Overview

Largest Production Deployment of AI and IoT Applications

Operational Impact

Quantitative Benefit