Utilizing machine learning models to estimate energy savings from an industrial energy system

Abstract

Energy audits are an important part of reducing energy usage, costs, and carbon emissions, but there have been discrepancies in the quality of audits depending upon the auditor, which can negatively affect the impacts and credibility of the energy assessment. In this paper, historical energy auditing data from a U.S. Department of Energy sponsored research program was gathered and analyzed with a machine-learning algorithm to predict demand savings from a compressed air system assessment recommendation in industrial manufacturing facilities. Different energy auditors calculate savings for repairing leaks in compressed air systems in various ways, so the energy demand savings have been calculated differently throughout the historical assessment recommendations. Machine learning models are utilized in order to enhance the accuracy of the existing practice and reduce variations resulting from the abovementioned discrepancies. A large set of historical assessment recommendation data was used to train five unique machine learning models. Four base learner models and one metalearner model were devised and compared. Results showed that the distributed random forest model best predicted compressed air energy demand savings against the new scenarios within an error of 17%. This indicates that the distributed random forest model can more accurately quantify savings from repairing leaks in compressed air systems. In addition, the results from this study provide insight into the important factors contributing to leaks in the compressed air systems and why it is crucial to repair those leaks regularly to save money and energy while decreasing emissions.

Keywords

• Compressed air system
• Energy efficiency
• Supervised machine learning
• Compressed air leaks
• Industrial energy audits