Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

Sean Kapp, Jun Ki Choi, Taehoon Hong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. New modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

Original languageEnglish
Article number113045
JournalRenewable and Sustainable Energy Reviews
Publication statusPublished - 2023 Feb

Bibliographical note

Funding Information:
We would like to express our gratitude to the US Department of Energy for supporting this work through their funding of the Industrial Assessment Center program ( DE-EE0009721 ). We thank previous and current UD-IAC students for their contributions to this continuing effort and our industrial partners for their significant contributions.

Publisher Copyright:
© 2022 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment


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