C3 IoT Case Studies Enterprise Data Analytics Platform and AMI Operations

	Enterprise Data Analytics Platform and AMI Operations C3 IoT

Enterprise Data Analytics Platform and AMI Operations

C3 IoT

Technology Category	Analytics & Modeling - Real Time Analytics Application Infrastructure & Middleware - Data Exchange & Integration Application Infrastructure & Middleware - Data Visualization Functional Applications - Remote Monitoring & Control Systems Platform as a Service (PaaS) - Data Management Platforms Sensors - Utility Meters
Applicable Industries	Utilities
Applicable Functions	Business Operation
Use Cases	Advanced Metering Infrastructure
Challenge	In tandem with its 6 year-long smart meter rollout plan, Con Edison sought to implement Advanced Metering Infrastructure (AMI) operations on top of a comprehensive enterprise data analytics platform for improved operational insight and customer service for its base of more than four million customers. In order to improve customer service and operations across its region, one of the largest integrated utilities in the United States has rolled out the C3 AI Suite and C3 AMI Operations application on AWS. Con Edison’s project objectives were to deliver on the utility’s commitments for presenting customer data, establish AMI operations across 5 million smart meters to ensure operational health, and build a federated data image platform for analytic capabilities. The utility’s smart meter deployment will generate between 100 terabytes and 1 petabyte of data per year, so choosing a platform that could scale and continue to perform analytics on an ever-larger data set was vital. Read More
About Customer	Major Electric and Gas Distribution company headquartered in New York City- $13B Annual Revenue- 4.4M Customers in Manhattan and the NYC metro area- 15,000 Employees- 5.3M AMI Meter Deployment Read More
Customer Name	Con Edison Read More
Solution	To put the foundational enterprise data analytics platform in place, C3.ai and the utility worked together to aggregate two years of data from 13 source systems covering 5 million customer accounts. The joint team then designed requirements for third-part data use and configuration of the basic AMI Operations application. Once the integrated data image was in place, the team configured two machine learning algorithms and 50 analytics to identify deployment and installation issues and determine meter and network health. The utility can now monitor smart meter deployment to identify any installation or configuration issues. The application also provides real-time status at any level of aggregation—from the individual meter to the overall system—and a prioritized list of meters that require attention. In future phases, the company plans to build on its enterprise data analytics plan for additional customer insight applications and distribution and transmission automation capabilities. Project Highlights: - 10 Month Project - 13 Source Systems - 5M Customer Accounts - 180B+ Annual Rows of Data Integrated Read More Log in to view content
Data Collected	Electricity Meters Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Real Time Analytics

Application Infrastructure & Middleware - Data Exchange & Integration

Application Infrastructure & Middleware - Data Visualization

Functional Applications - Remote Monitoring & Control Systems

Platform as a Service (PaaS) - Data Management Platforms

Sensors - Utility Meters

Applicable Industries

Utilities

Applicable Functions

Business Operation

Use Cases

Advanced Metering Infrastructure

Challenge

In tandem with its 6 year-long smart meter rollout plan, Con Edison sought to implement Advanced Metering Infrastructure (AMI) operations on top of a comprehensive enterprise data analytics platform for improved operational insight and customer service for its base of more than four million customers. In order to improve customer service and operations across its region, one of the largest integrated utilities in the United States has rolled out the C3 AI Suite and C3 AMI Operations application on AWS. Con Edison’s project objectives were to deliver on the utility’s commitments for presenting customer data, establish AMI operations across 5 million smart meters to ensure operational health, and build a federated data image platform for analytic capabilities.

The utility’s smart meter deployment will generate between 100 terabytes and 1 petabyte of data per year, so choosing a platform that could scale and continue to perform analytics on an ever-larger data set was vital.

About Customer

Major Electric and Gas Distribution company headquartered in New York City- $13B Annual Revenue- 4.4M Customers in Manhattan and the NYC metro area- 15,000 Employees- 5.3M AMI Meter Deployment

Customer Name

Con Edison

Solution

To put the foundational enterprise data analytics platform in place, C3.ai and the utility worked together to aggregate two years of data from 13 source systems covering 5 million customer accounts. The joint team then designed requirements for third-part data use and configuration of the basic AMI Operations application. Once the integrated data image was in place, the team configured two machine learning algorithms and 50 analytics to identify deployment and installation issues and determine meter and network health.

The utility can now monitor smart meter deployment to identify any installation or configuration issues. The application also provides real-time status at any level of aggregation—from the individual meter to the overall system—and a prioritized list of meters that require attention. In future phases, the company plans to build on its enterprise data analytics plan for additional customer insight applications and distribution and transmission automation capabilities.

Project Highlights:

- 10 Month Project

- 13 Source Systems

- 5M Customer Accounts

- 180B+ Annual Rows of Data Integrated

Data Collected

Electricity Meters

Impact #1	[Efficiency Improvement - Asset Monitoring] 2 machine learning algorithms and 50 analytics to identify deployment and installation issues and determine meter and network health.
Impact #2	[Data Management - Real Time Data Analysis] Real-time status at any level of aggregation—from the individual meter to the overall system—and a prioritized list of meters that require attention.

Impact #1

[Efficiency Improvement - Asset Monitoring]

2 machine learning algorithms and 50 analytics to identify deployment and installation issues and determine meter and network health.

Impact #2

[Data Management - Real Time Data Analysis]

Real-time status at any level of aggregation—from the individual meter to the overall system—and a prioritized list of meters that require attention.

Benefit #1	$854M+ Identified annual customer benefit
Benefit #2	2300+ Deployment issues identified in 4 months
Benefit #3	5 External systems accessing unified data

Benefit #1

$854M+ Identified annual customer benefit

Benefit #2

2300+ Deployment issues identified in 4 months

Benefit #3

5 External systems accessing unified data

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Overview

Enterprise Data Analytics Platform and AMI Operations

Operational Impact

Quantitative Benefit