State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Amir Mosavi,Amir Mosavi,Amir Mosavi,Mohsen Salimi,Sina Ardabili,Timon Rabczuk,Shahaboddin Shamshirband,Annamária R. Várkonyi-Kóczy +7 more
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TLDR
There is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models.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.read more
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Tackling Climate Change with Machine Learning
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References
More filters
Journal ArticleDOI
k -anonymity: a model for protecting privacy
TL;DR: The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
Journal ArticleDOI
Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads
Peter Palensky,Dietmar Dietrich +1 more
TL;DR: An overview and a taxonomy for DSM is given, the various types of DSM are analyzed, and an outlook on the latest demonstration projects in this domain is given.
Journal ArticleDOI
Machine learning methods for solar radiation forecasting: A review
Cyril Voyant,Gilles Notton,Soteris A. Kalogirou,Marie Laure Nivet,Christophe Paoli,Christophe Paoli,Fabrice Motte,Alexis Fouilloy +7 more
TL;DR: An overview of forecasting methods of solar irradiation using machine learning approaches is given and it will be shown that other methods begin to be used in this context of prediction.
Journal ArticleDOI
A review of data-driven building energy consumption prediction studies
Kadir Amasyali,Nora El-Gohary +1 more
TL;DR: A review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation is provided in this paper.
Journal ArticleDOI
Electric Energy Systems Theory: An Introduction
Olle I. Elgerd,H. H. Happ +1 more
TL;DR: In this second edition the introductory chapters have been strengthened to improve appeal to students, and new problems and material has been added on system protection.