Columbia University

Technology Ventures

Machine learning energy consumption forecasting system

Technology #cu13111

By using initial energy usage data from a given manufacturing facility, as well as seasonal patterns and weather predictions, the machine learning forecasting system can predict the facility’s future electricity consumption. The system ties into electric vehicle (EV) recharging infrastructure associated with the facility, optimizing EV recharging loads and schedules based on usage and weather information. By comparing actual usage with usage predictions, the forecasting system continues to learn from the manufacturing facility it monitors, becoming more accurate and saving additional money and energy the longer the forecasting system is used.

A continuously learning energy forecasting system is scalable and saves costs over time

The feedback loop built into the machine learning forecasting system scores the accuracy of its own predictions, minimizing errors and inefficiencies over time. The system thus gets better at helping manufacturing facilities and companies avoid peak consumption penalties. It allows companies to more efficiently use their capital assets, extending the lifetime of those assets and reducing expeditures. The system is scalable to hundreds of EVs associated with the manufacturing facility, and has been demonstrated in a package delivery facility that processes tens of thousands of packages per day.

Lead Inventor:

Roger N. Anderson, Ph.D.


  • Electricity consumption management for manufacturing and delivery facilities
  • Load prediction and assignments for electric vehicle recharging stations
  • Energy management within power plants and smart grids
  • Energy management and consumption predictions for municipalities


  • Machine learning system becomes increasingly accurate over time
  • System integrates with electric vehicle (EV) charging station, and is scalable to cover thousands of EVs
  • Allows manufacturing and processing facilities to avoid peak consumption penalties
  • Greater efficiency in consuming electricity means facilities using the system can reduce capital expenses and extend the lifetime of capital equipment

Patent information:

Patent Pending (WO/2013/023178)

Licensing Status:

Available for licensing and sponsored research support

Tech Ventures Reference: IR CU13111

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