Next-Level Energy Management in Manufacturing: Facility-Level Energy Digital Twin Framework Based on Machine Learning and Automated Data Collection

David Vance, Mingzhou Jin (), Thomas Wenning, Sachin Nimbalkar and Christopher Price
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David Vance: Industrial and Systems Engineering Department, Institute for a Secure and Sustainable Environment, The University of Tennessee, Knoxville, TN 37919, USA
Mingzhou Jin: Industrial and Systems Engineering Department, Institute for a Secure and Sustainable Environment, The University of Tennessee, Knoxville, TN 37919, USA
Thomas Wenning: Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Sachin Nimbalkar: Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Christopher Price: Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA

Energies, 2025, vol. 18, issue 13, 1-28

Abstract: This research introduces an energy prediction framework at the facility level supported by automated data collection and machine learning models. It investigates whether reducing the prediction time scale allows for applying more complex machine learning techniques and if those techniques improve the prediction accuracy. The primary advantages of this framework lie in its automation of the energy prediction process and its provision of real-time energy data suitable for use in energy dashboards or digital twins. A sitewide dataset was created by combining 15 min energy and daily production data of five shops—assembly, battery, body (electric), body (gas), and paint—from a globally recognized electric vehicle manufacturer. Various machine learning models were evaluated on daily, weekly, and monthly datasets, including, in increasingly complex order: naïve, simple linear regression, net regularized generalized linear regression, principal component regression, k-nearest neighbor, random forest, and Bayesian regularized neural network. Compared to the current state-of-the-art energy consumption prediction for the industrial facility level, this research investigates more complex models and smaller time intervals for higher accuracy. The findings revealed that the more complex monthly models require a minimum of a year and a half of data to operate, while weekly models demand a year of data to achieve improved accuracy. Daily models can operate with only six months of data but exhibit poor performance due to reduced prediction accuracy of production. Key challenges identified include access to reliable, high-quality energy and production data and the initial demand for human labor.

Keywords: energy management; machine learning; digital twin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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