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.

Key Tasks & Milestones

  • Process Review to identify areas of energy use, ambient conditions, schedule, and to establish and test data at two plant sites
  • Software Development for energy modeling, aggregation of energy data, and energy analytics
  • Development of tools for energy prediction, datasets; and creation of machine learning to detect dependent variables in consumption patterns
  • Development of energy model and innovation workshop set up to review project
  • Development of a public energy Ontology for use by CESMII community
  • Development of marketing material; generate white paper/video testimonials to enable widespread use of developed methods

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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