T&D World Magazine
REVENUE PROTECTION OPPORTUNITY DETAIL
REVENUE PROTECTION OPPORTUNITY DETAIL

Baltimore Gas and Electric Deploys Smart Grid Analytics

The two C3 Energy Smart Grid Analytics solutions analyze over 35 billion rows of data from 12 BGE and third-party data sources.

Baltimore Gas and Electric Co. has deployed C3 Revenue Protection and C3 AMI Operations across BGE’s two million meter smart grid network. The two C3 Energy Smart Grid Analytics solutions analyze over 35 billion rows of data from 12 BGE and third-party data sources to provide critical, actionable insight to the BGE revenue protection and grid operational teams. The data sources include MDMS, head end, CIS, work management, EAM, OMS, GIS, CMS, and meter installation vendor systems, among others.

BGE serves over 1.2 million electric customers and more than 650,000 gas customers in a 2,300-square-mile area encompassing Baltimore City and all or part of ten counties in Central Maryland. C3 Energy and Accenture worked closely with BGE’s IT and business teams through the process of integrating, implementing, user testing, and launching the solutions. C3 Revenue Protection identifies load profile anomalies and outliers, while C3 AMI Operations validates the operational functionality and integrity of the automated metering infrastructure (AMI) network, helping the operational team ensure high levels of service reliability for BGE customers. Together, the two solutions reduce instances of unbilled energy at BGE.

The initial results from the two solutions put BGE on track to achieve its goals for reductions in unbilled energy, which is expected to deliver tens of millions of dollars per year in economic value to BGE and its customers.

"The BGE implementation of C3 Energy Smart Grid Analytics is one of the most sophisticated examples of machine learning in power delivery today. The system analyzes data from hundreds of thousands of devices, then generates hundreds of analytic features per device, all in real time," said J. Zico Kolter, Assistant Professor of Computer Science, Carnegie Mellon University. “The machine-learning algorithms then use this data to evolve automatically over time, ensuring the effective resolution of grid operational issues.”

 

Hide comments

Comments

  • Allowed HTML tags: <em> <strong> <blockquote> <br> <p>

Plain text

  • No HTML tags allowed.
  • Web page addresses and e-mail addresses turn into links automatically.
  • Lines and paragraphs break automatically.
Publish