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

Abdulgani Kahraman, Mehmed Kantardzic, Muhammet Mustafa Kahraman and Muhammed Kotan
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Abdulgani Kahraman: Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
Mehmed Kantardzic: Department of Computer Science and Engineering, University of Louisville, Louisville, KY 40292, USA
Muhammet Mustafa Kahraman: Department of Mining Engineering, Gumushane University, Gumushane 29100, Turkey
Muhammed Kotan: Department of Information Systems Engineering, Sakarya University, Sakarya 54050, Turkey

Energies, 2021, vol. 14, issue 20, 1-17

Abstract: 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.

Keywords: energy efficiency; energy consumption prediction; concept drift; deep learning; industrial machines (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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