Driving Network Efficiency and Fraud Detection Efforts
  • Case Study

Driving Network Efficiency and Fraud Detection Efforts

BGE driving network efficiency and fraud detection efforts

Project Challenge

In 2014, Baltimore Gas and Electric Company (BGE), a subsidiary of Exelon Corporation, launched C3.ai applications across all two million sensors and devices in its service territory. BGE now uses three C3.ai applications to deliver an annual economic benefit of $20 million to BGE and its customers. C3 AMI Operations™ optimizes the deployment and ongoing health of its advanced metering infrastructure (AMI) network. C3 Revenue Protection™ identifies and reduces unbilled energy usage. C3 Energy Intelligence™ provides business intelligence analysts with ad-hoc reporting and dashboarding functionality across 12 unique source systems.



Weeks from project kickoff to launch


C3.ai Applications


Accuracy rate, growing to 99%


of target business value $15.4M to date

Project Highlights


Unique Source Systems


of New Data Analyzed Each Day


Federated Cloud Image of Data


Complex Analytics In Use

Integrating Existing Systems

C3.ai delivered the three applications on schedule, in six months from project kick-off to launch. The three applications are built on the C3 AI Suite and run on the AWS Cloud.

Deployment involved developing 42 integrations to 12 unique source systems. C3.ai loaded two years of historical BGE data in a 10 TB unified cloud image with 35 billion rows of data aggregated, federated, and analyzed. To build the three applications and match BGE’s requirements and available data, the C3.ai team configured more than 140 complex machine learning features, comprised of 650 individual rules, that provide ongoing predictive recommendations in a live production application. In total, more than 8 GB and 220 million rows of new data are delivered each day to the C3 AI Suite on AWS. The machine learning algorithms leverage Amazon EC2 instances, running on Intel® Xeon® processors designed to power compute-intensive workloads.

Solution Architecture

Origin Platform Architecture

Project Timeline

BGE Project Timeline

Previous Case Study

Fortune 100 Tech Company: Execute Energy and 
Sustainability Management Solutions

Next Case Study

AEP-PSO: Improving Customer Service via Energy Consumption Reporting