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

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.
- An Giang University Viet Nam
- Ton Duc Thang University Viet Nam
- Bauhaus University, Weimar Germany
- Niroo Research Institute Iran (Islamic Republic of)
- K.N.Toosi University of Technology Iran (Islamic Republic of)
blockchain, internet of things (IoT), Technology, 330, wavelet neural network (WNN), forecasting, remote sensing, energy informatics, big data, energy systems, neuro-fuzzy, ANFIS, energy demand, Energy systems, T, artificial neural networks (ANN), ensemble, deep learning, prediction, hybrid models, 620, decision tree (DT), machine learning, support vector machines (SVM), smart sensors, renewable energy systems
blockchain, internet of things (IoT), Technology, 330, wavelet neural network (WNN), forecasting, remote sensing, energy informatics, big data, energy systems, neuro-fuzzy, ANFIS, energy demand, Energy systems, T, artificial neural networks (ANN), ensemble, deep learning, prediction, hybrid models, 620, decision tree (DT), machine learning, support vector machines (SVM), smart sensors, renewable energy systems
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).394 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 0.1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 0.1% visibility views 3 download downloads 38 - 3views38downloads
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