Predicting Energy Consumption of Production Facilities for an Energy Management Software company with ~97% accuracy

Case Study

Predicting Energy Consumption of Production Facilities for an Energy Management Software company with ~97% accuracy 

Business Objective

Our client is a product resource management company. The client wanted to identify opportunity areas for a leading manufacturer to reduce their energy resource consumption and carbon footprint across their production facilities.

The client wanted to use historical machine-level production outputs to accurately estimate energy resources required per machine and subsequently take corrective action to reduce energy inefficiencies within their production processes.

Challenges

  • Sparse facility output data to identify the right relationships and not over-parameterize or over-fit the model
  • The energy consumption at production facilities also depended on several undocumented sources of energy consumption such as HVAC
  • Observed temporal relationships between the machines that are generally difficult to capture using sparse data

Solution Methodology 

  • Worked with the client to understand the available data in the context of the business problem
  • Explored the non-linear and temporal relationships of resource consumption with run times and production outputs of various machines
  • Identified the right model forms through systematic analyses using training and test datasets,  and computed the confidence around our estimates for various machines
  • Used bootstrapping to analyze the sensitivity of end-results and assumptions, and refined the algorithms, thus ensuring their robustness

Business Impact

  • Our model predicted the overall energy consumption of the production facilities with up to 97% accuracy
  • Identified areas where energy efficiency could be gained by modifying the production schedule
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