Modeling Energy Consumption Using Machine Learning - Sarswatula, Sai Aravind; Pugh, Tanna; Prabhu, Vittaldas (Frontiers in Manufacturing Technology, 2022)
Electrical, metal, plastic and food manufacturing are among the major energy consuming industries in the U.S. Since 1981 the U.S. Department of Energy (DOE) Industrial Assessments Centers (IACs) have conducted audits to track and analyzes energy data across several industries and provided recommendations for improving energy efficiency. In this paper we use statistical and machine learning techniques to draw insights from this IAC dataset with over 15,000 samples collected from 1981 to 2013. We developed predictive models for energy consumption using the machine learning techniques such as Multiple Linear Regression, Random Forest Regressor, Decision Tree Regressor and Extreme Gradient Boost Regressor. We also developed classifier models using Support Vector Machines, Random Forest, K-Nearest Neighbor (KNN), and deep learning. Results using this data set indicate that Random Forest Regressor is the best prediction technique with an R2 of 0.869 and Random Forest classifier is the best technique with precision, recall, F1 score, and accuracy of 0.818, 0.884, 0.844, and 0.883, respectively. Deep learning also performed competitively with an accuracy of about 0.88 in training and testing after 10 epochs. The machine learning models could be useful in benchmarking energy consumption of factories and identifying opportunities to improve energy efficiency.