Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Computer Engineering and Computer Science

Degree Program

Computer Science and Engineering, PhD

Committee Chair

Kantardzic, Mehmed

Committee Co-Chair (if applicable)

Elmaghraby, Adel

Committee Member

Elmaghraby, Adel

Committee Member

Kumar, Anup

Committee Member

Chang, Dar-Jen

Committee Member

Lewis, James E.

Author's Keywords

Energy efficiency; energy consumption prediction; concept drift; data-driven deep learning


This thesis focuses on methods for improving energy consumption prediction performance in complex industrial machines. Working with real-world industrial machines brings several challenges, including data access, algorithmic bias, data privacy, and the interpretation of machine learning algorithms. To effectively manage energy consumption in the industrial sector, it is essential to develop a framework that enhances prediction performance, reduces energy costs, and mitigates air pollution in heavy industrial machine operations. This study aims to assist managers in making informed decisions and driving the transition towards green manufacturing. The energy consumption of industrial machinery is substantial, and the recent increase in CO2 emissions is a major concern. Consequently, energy efficiency is becoming gradually more crucial for businesses, governments, and the environment. Accurately estimating the power consumption of industrial machines might be useful in adjusting machine operation settings for operators. Smart devices can help manage electricity consumption more efficiently by providing necessary data that can be used to make better decisions. However, datasets from real-world industrial machines often contain several challenges, including unexpected changes over time, inconsistent operating conditions, missing data, unknown working environments, unrecorded maintenance, and human errors. The utilization of energy consumption patterns can enable more accurate calculation methods. Precisely predicting the energy consumption of heavy industrial machines is fundamental for enhancing energy efficiency and minimizing blackouts. While numerous research papers have concentrated on improving the prediction accuracy of traditional static machine learning models, these models may not perform well as data evolves by time. The presence of expected or unexpected variations, known as concept drift, can have a significant impact on the performance of machine learning models over time. Therefore, it is crucial to design a method that takes these potential changes into account when predicting power consumption for industrial machines. A novel data-driven dynamic modeling approach was developed in this dissertation to detect repetitive machine running regimes and improve the prediction accuracy of energy usage for industrial machines. All designed methods, including traditional static modeling, were applied to three distinct real-world industrial machine datasets. The experimental results obtained from these implementations are thoroughly discussed and presented in detail. In the first part of the dissertation, the proposed multi-regime approach successfully detects repeated machine multi-regime running conditions and maintains a better overall prediction performance over time compared to other methods. Secondly, the designed dynamic method for predicting energy consumption of industrial machines under repetitive regimes outperforms traditional static modeling and ensemble modeling in real-time prediction. Lastly, the implemented statistical tests demonstrated that the proposed dynamic method achieved a significant improvement in prediction performance accuracy compared to the other applied methods. According to the experimental results, the developed dynamic modeling proves to be applicable to various industrial machines with complex structures and features, delivering a more precise prediction performance. As a result of this study, companies can gain a better understanding of their machine working conditions and make necessary adaptations to decrease their energy consumption over time. Overall, this dissertation emphasizes the importance of industrial machine data in the energy sector and the potential for data-driven solutions to enhance energy efficiency in industrial machinery. The ultimate goal is to encourage further research in energy efficiency with the aim of reducing air pollution and facilitating a timely transition towards green manufacturing.