conEdison logoCurrent and recent Con Edison projects illustrate how the Center is applying machine learning to crucial challenges confronted by the public utility providing electricity and gas to the New York City metropolitan area.

Con Edison’s Challenge: To ensure compliance with regulatory requirements and for legal reasons, Con Edison had saved over twenty years of records. New York City was one of the earliest major cities to be electrified over 180 years ago. Unlike more modern systems in cities like Tokyo and Las Vegas, most of the 30,000 plus cable sections of paper insulated lead sheathed cable, each a block long, are aging. Research Scientists at the Center are using machine learning capabilities to understand patterns (including 400 mgs of data about feeder failures daily), then predict which components are most susceptible to feeder failure and which are most likely to work well, based on age, location, weather and other conditions as well as historical events.

The Need to Understand Patterns of Failure: Because the maintenance and repairs of each of the 30,000+ cable section involves a series of tasks from pumping water and vermin out of manholes, shutting down power safely, pulling out old cable out of 3-16 splices at each end of the section and replacing them with new cable.

The Value of the Outcome: The work is increasing predictability of when and where feeders in the distribution system are susceptible to future failure. In 2005, CCLS algorithms, incorporating multiple attributes with the MartiRank method, predicted 40 percent of future outages for the data they had reviewed. As the computational systems “learn” more about the data, they reconfigure themselves. As the system generates models, CCLS research scientists refine rules, with the goal of moving towards 80 percent accuracy. This predictability gives Con Edison information needed to schedule maintenance in ways that will produce a geometric improvement in continuous power service to millions of customers and a reduction of costly emergency service.

CCLS wins GE Ecomagination Innovation Prize

The CCLS submission “Creation of a Columbia Machine Learning System to Optimize the Recharging of Electric Delivery Vehicles in Large Urban Cities” has been chosen as the “Winning University Program” in General Electric’s Ecomagination Innovation Challenge of 2010.GE’s CEO Jeff Immelt presented the award funding the 3 year, $1.1 million project, at a live event in New York City on Novem

Columbia Working with Con Edison on Smart Grid Technology

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Patented controller will
deliver power quickly and cheaply 

Software Project Pricing models

With the current focus on demonstrating the value of the projects we do for Con-Edison, I have been reading up on the different methodologies that can be used for evaluating projects. In addition, as part of the Technology in Business Management course that I am taking, we had gone through 2 of them - the Net Present Value and the Options Pricing Model. The book - Strategic Decision Making for IT Managers by Lucas (1999), briefly explains these two methods as well.

Spotlight on Research: ConEd and Columbia Work to Prevent Power Outages

Spotlight on Research: ConEd and Columbia Work to Prevent Power Outages

posted 06/01/05

Spotlight on Research: ConEd and Columbia Work to Prevent Power Outages

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