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State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

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School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
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Centre for Accident Research Road Safety-Queensland, Queensland University of Technology, Brisbane QLD 4059, Australia
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Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, Budapest, Hungary
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Department of Renewable Energies, Niroo Research Institute, Tehran, Iran
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Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
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Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Department of Mathematics and Informatics, J. Selye University, Komarno 94501, Slovakia
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Author to whom correspondence should be addressed.
Energies 2019, 12(7), 1301; https://doi.org/10.3390/en12071301
Received: 12 February 2019 / Revised: 20 March 2019 / Accepted: 28 March 2019 / Published: 4 April 2019
Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability. View Full-Text
Keywords: energy systems; machine learning; artificial neural networks (ANN); support vector machines (SVM); neuro-fuzzy; ANFIS; wavelet neural network (WNN); big data; decision tree (DT); ensemble; hybrid models; deep learning; blockchain; renewable energy systems; energy informatics; internet of things (IoT); smart sensors; remote sensing; prediction; forecasting; energy demand energy systems; machine learning; artificial neural networks (ANN); support vector machines (SVM); neuro-fuzzy; ANFIS; wavelet neural network (WNN); big data; decision tree (DT); ensemble; hybrid models; deep learning; blockchain; renewable energy systems; energy informatics; internet of things (IoT); smart sensors; remote sensing; prediction; forecasting; energy demand
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Mosavi, A.; Salimi, M.; Faizollahzadeh Ardabili, S.; Rabczuk, T.; Shamshirband, S.; Varkonyi-Koczy, A.R. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. Energies 2019, 12, 1301.

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