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

Authors: Shahaboddin Shamshirband; Annamária R. Várkonyi-Kóczy; Mohsen Salimi; Sina Ardabili; Amir Mosavi; Amir Mosavi; Amir Mosavi; +1 Authors

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

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.

Country
Australia
Keywords

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

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    Top 0.1%
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    Top 0.1%
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    visibility views 3
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download
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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
394
Top 0.1%
Top 1%
Top 0.1%
3
38
Green
gold