Smart Grid Webinar:
Fighting Energy Theft with Analytics

Date: Mar 27, 2013

Time: 2:00 ET PM

Register Now

Energy theft costs consumers billions of dollars every year as utilities recoup losses in the form of increased rates. The FBI and International Utility Revenue Protection Association estimate that energy theft drains the U.S. economy of $6 billion annually – about the same as loss caused by auto theft. The Edison Institute estimates that the prevention of electricity theft and retail customer payment default are together a $5 billion business opportunity.

Many utilities are using increasingly sophisticated analytics to identify possible electricity diversion, and while smart grid technology has created new ways to catch energy thieves, it has also created novel ways to steal energy. How are utilities facing this challenge?

Chartwell’s webinar, Smart Grid: Fighting Energy Theft with Analytics, brings together subject matter experts with groundbreaking initiatives aimed at identifying, stopping and preventing energy theft. Learn workable methods to catch energy thieves from Oncor and Fujitsu Laboratories of America.

Your webinar registration includes:

  • A site license to attend (invite as many people as you can fit around your computer and telephone).
  • The opportunity to connect directly with our speakers during audience Q&A.


  • Jonathan Pettit, AMS Program Manager, Oncor
  • Randall Schmidt, Supervisor AMS Operations, Oncor

  • Daisuke Mashima, Research Staff, Fujitsu Laboratories of America Inc.
    Daisuke is a member of the research staff in SEDN (Solution for Electricity Distribution Networks) Group. His current research focuses on cyber security and privacy in smart grid networks, including anomaly detection, applied cryptography, and network security. Before joining Fujitsu Laboratories of America in 2012, Daisuke worked at eBay Inc., AT&T Labs Research, and Palo Alto Research Center (PARC) where he was engaged in a variety of research projects, namely security metrics, information visualization, and social network analysis.

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