C3 IoT > Case Studies > Enterprise Data Analytics Platform and AMI Operations

Enterprise Data Analytics Platform and AMI Operations

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 Enterprise Data Analytics Platform and AMI Operations - IoT ONE Case Study
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
The Customer
About The 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
The 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.

The 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
Operational Impact
  • [Efficiency Improvement - Asset Monitoring]

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

  • [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. 

Quantitative Benefit
  • $854M+ Identified annual customer benefit

  • 2300+ Deployment issues identified in 4 months

  • 5 External systems accessing unified data

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