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  • Enel.com
  • Nov 11, 2015

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C3 Predictive Maintenance Receives Top Honors for Improving Energy Distribution Network Performance and Reducing Asset and System Management Risks and Costs

Redwood City, Calif. – November 11, 2015 – C3 Energy, the leader in enterprise analytic software solutions for the global energy industry, today announced that its C3 Predictive Maintenance cloud-based software application has been recognized in the 2015 Fierce Innovation Awards.

C3 Predictive Maintenance received top honors based on its unique ability to help utilities accurately prioritize and optimize network asset management through advanced, cost effective technologies such as cloud computing, big data analytics, and machine learning. Through improved reliability and decreased maintenance costs, C3 Predictive Maintenance delivers an estimated recurring annual economic benefit of $20 per meter to a utility and its customers. Utility deployments of C3 Predictive Maintenance at scale today are addressing more than 15,000 distribution feeders and 230,000 secondary substations, serving approximately 15 million customers.

An elite panel of judges composed of energy industry experts from Ameren Corporation, CenterPoint Energy, Commonwealth Edison, Duke Energy, Iberdrola USA, PECO, and San Diego Gas & Electric, evaluated solutions based on technology innovation, financial impact, market validation, compatibility with existing network environments, end-user customer experience, and overall level of innovation.

“Utilities are under increasing pressure to improve grid reliability while decreasing operating costs. Shifting from reactive to predictive maintenance can mitigate the risks and expenses associated with asset failure and emergency replacements,” said Ed Abbo, president and CTO of C3 Energy. “C3 Predictive Maintenance enables utilities – as well as power generators, oil and gas companies, and, really, any organization with industrial equipment requiring continuous monitoring and regular maintenance – to understand and assess system risk and reliability in near-real time, identify high-risk assets before they fail, reduce unexpected capital and operating expenditures, and reduce unplanned outages.