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state of the art of machine learning models in energy systems, a systematic review

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.Faculty of Technology, Design and Environment\School of the Built EnvironmentRADAR: Research Archive and Digital Asset Repository

Machine learning researchers, on the other hand, have been mainly focused on producing high accurate models without considering energy consumption as an important factor . Computer architecture researchers have been investigating energy consumption for decades, especially to be able to deliver state-of-the-art energy efficient processors. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.Faculty of Technology, Design and Environment\School of the Built EnvironmentRADAR: Research Archive and Digital Asset Repository School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UKCentre for Accident Research Road Safety-Queensland, Queensland University of Technology, Brisbane QLD 4059, AustraliaInstitute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, Budapest, HungaryDepartment of Renewable Energies, Niroo Research Institute, Tehran, IranBiosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, IranDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, VietnamFaculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, VietnamDepartment of Mathematics and Informatics, J. Selye University, Komarno 94501, SlovakiaAuthor to whom correspondence should be addressed. 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. 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.

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. Multiple requests from the same IP address are counted as one view. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. State of the Art of Machine Learning Models in Energy Systems, a Systematic Review. In this study, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Please note that many of the page functionalities won't work as expected without javascript enabled. Subscribe to receive issue release notifications and newsletters from MDPI journals This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability ... State of the art of machine learning models in energy systems, a systematic review. 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 review presents a review of 24 studies published in the past two years regarding state-of-the-art machine learning algorithms used for predicting energy consumption. 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.

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