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

Amir Mosavi, Mohsen Salimi, Sina Faizollahzadeh Ardabili, Timon Rabczuk, Shahaboddin Shamshirband and Annamaria R. Varkonyi-Koczy
Additional contact information
Amir Mosavi: School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
Mohsen Salimi: Department of Renewable Energies, Niroo Research Institute, Tehran, Iran
Sina Faizollahzadeh Ardabili: Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
Timon Rabczuk: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Annamaria R. Varkonyi-Koczy: Department of Mathematics and Informatics, J. Selye University, Komarno 94501, Slovakia

Energies, 2019, vol. 12, issue 7, 1-42

Abstract: 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.

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 (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (67)

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