Industrial machinery is a significant energy consumer, and its CO₂ emissions have increased dramatically in recent years. Therefore, energy efficiency is becoming crucial for businesses, governments, as well as the planet. Estimating the power consumption of industrial machines with greater accuracy assists management and optimizes machine operation parameters. Real-time industrial machine datasets present several challenges, such as changes in the data over time, unknown running conditions, missing data, etc. Most research publications focus on the accuracy of traditional static models of forecasting; however, prediction performance deteriorates over time because data evolves. We implemented deep learning as a prediction model for three distinct real-world industrial datasets. The proposed method, dynamic modeling with memory (DMWM), improved overall prediction performance compared with conventional approaches by identifying concept drifts and optimizing the number of required models in response to industrial datasets’ recurring machine energy consumption patterns.

There has been increasing interest in reducing carbon footprints globally in recent years. Hence increasing share of green energy and energy efficiency are promoted by governments. Therefore, optimizing energy consumption is becoming more critical for people, companies, industries, and the environment. Predicting energy consumption more precisely means that future energy management planning can be more effective. To date, most research papers have focused on predicting residential building energy consumption; however, a large portion of the energy is consumed by industrial machines. Prediction of energy consumption of large industrial machines in real time is challenging due to concept drift, in which prediction performance deteriorates over time. In this research, a novel data-driven method multi-regime approach (MRA) was developed to better predict the energy consumption for industrial machines. Whereas most papers have focused on finding an excellent prediction model that contradicts the no-free-lunch theorem, this study concentrated on adding potential concept drift points into the prediction process. A real-world dataset was collected from a semi-autonomous grinding (SAG) mill used as a data source, and a deep neural network was utilized as a prediction model for the MRA method. The results proved that the MRA method enables the detection of multi-regimes over time and provides a highly accurate prediction performance, thanks to the dynamic model approach.

Cardiovascular disease continues to cause an important global health challenge, highlighting the critical importance of early detection in mitigating cardiac-related issues. There is a significant demand for reliable diagnostic alternatives. Taking advantage of health data through diverse machine learning algorithms may offer a more precise diagnostic approach. Machine learning-based decision support systems that utilize patients’ clinical parameters present a promising solution for diagnosing cardiovascular disease. In this research, we collected extensive publicly available healthcare records. We integrated medical datasets based on common features to implement several machine learning models aimed at exploring the potential for more robust predictions of cardiovascular disease (CVD). The merged dataset initially contained 323,680 samples sourced from multiple databases. Following data preprocessing steps including cleaning, alignment of features, and removal of missing values, the final dataset consisted of 311,710 samples used for model training and evaluation. In our experiments, the CatBoost model achieved the highest area under the curve (AUC) of up to 94.1%.

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