Inferential Modeling for Driving Out Energy Waste

Project Lead: ThinkIQ
Partners: General Mills

Member % Cost Share: 36%
CESMII % Cost Share: 64%
Duration: 12 Months

Problem Statement

Energy loses are largely invisible on the manufacturing floor. Wasted energy typically goes unnoticed for long periods of time. And it has proven difficult for manufacturers to determine when and where unexpected or unknown energy losses are occurring.

Project Goal

The goal of this project will drive out wasted energy in manufacturing facilities through improved information technology. The primary objective of this project will provide technical staff with technology to sniff out unknown or unexpected energy consumption across the facility. Prove the hypothesis that energy waste can be detected without the use of expensive energy meters.

Technical Approach

Create and apply new data modeling and analytics technology to quickly point manufacturers to energy opportunities by identifying periods of high unknown energy consumption.

Deliverables/Outcomes/SM Marketplace

  • Develop machine learning and data centric analytics and initial machine learning capabilities to be implemented in the manufacturing process.

  • Development, testing and CESMII platform integration of Semantic model of energy usage linked to material ledger, analytics tools to detect anomalies, and user interfaces.

  • Demonstrate the generalized semantic model focused on equipment and energy objects in a workshop with CESMII.

  • Equipment and process energy models for a generic factory environment

  • Pump SM profile

  • Mixer SM profile

  • Tank SM profile

  • Python based machine learning predictive tools for power consumption and estimating energy usage at equipment and plant level

  • Energy management tools for the CESMII SMIP

Potential Impact

  • Project can be expanded to address all of the common equipment that make up the majority of US Manufacturing
  • 5 percent reduction in energy wastage will result in national savings in excess of $2B
  • Expansion of American manufacturing without having to build new power generation units
  • Lower emissions in the environment


  • Fast adoption across many CESMII members manufacturing sites at once
  • Leveraging existing networks of system integrators with strong IT/OT experience
  • Software configuration is cloud based enabling engineering teams to perform configuration tasks remote from the manufacturing plant

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