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 Industrial Assessments Centers (IACs) have conducted audits to track and analyze energy data across several industries and provided recommendations for improving energy efficiency. In this article, we used 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 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 the 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 the energy consumption of factories and identifying opportunities to improve energy efficiency.
Enterprises need to make continuous fundamental transformations—such as improving current business processes, performing entirely different tasks, and conducting automated business processes—to maintain or gain competitive advantage. These transformations may increase value or decrease time, costs, and uncertainties. However, it is difficult to choose transformations that deserve major investment without assessing the relative value of alternative transformations. Analyzing and redesigning business processes to ensure consistency with business requirements and information technology (IT) specifications is a critical factor for successful enterprise transformation. This paper provides an evolution methodology based on process complexity to implement effective and efficient best practices for enterprise transformation. This paper uses a process complexity and usability metrics, combining software science and cognitive science, to evaluate the cognitive loading of the business processes. Furthermore, to illustrate the metric, this paper describes an IT-driven enterprise transformation to enable university–industry collaboration. The purpose of this study is to evaluate the need for conducting operations with and without the use of information technology. The complexity model shows a more than 60% decrease in the complexity, suggesting that the IT-integrated process is less complex than earlier processes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.