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Article

A Data-Driven Multi-Regime Approach for Predicting Energy Consumption

1
Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
2
Department of Mining Engineering, Gumushane University, Gumushane 29100, Turkey
3
Department of Information Systems Engineering, Sakarya University, Sakarya 54050, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Morán
Energies 2021, 14(20), 6763; https://doi.org/10.3390/en14206763
Received: 2 September 2021 / Revised: 22 September 2021 / Accepted: 14 October 2021 / Published: 17 October 2021
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. View Full-Text
Keywords: energy efficiency; energy consumption prediction; concept drift; deep learning; industrial machines energy efficiency; energy consumption prediction; concept drift; deep learning; industrial machines
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MDPI and ACS Style

Kahraman, A.; Kantardzic, M.; Kahraman, M.M.; Kotan, M. A Data-Driven Multi-Regime Approach for Predicting Energy Consumption. Energies 2021, 14, 6763. https://doi.org/10.3390/en14206763

AMA Style

Kahraman A, Kantardzic M, Kahraman MM, Kotan M. A Data-Driven Multi-Regime Approach for Predicting Energy Consumption. Energies. 2021; 14(20):6763. https://doi.org/10.3390/en14206763

Chicago/Turabian Style

Kahraman, Abdulgani, Mehmed Kantardzic, Muhammet M. Kahraman, and Muhammed Kotan. 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption" Energies 14, no. 20: 6763. https://doi.org/10.3390/en14206763

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