Machine Learning in Smart Grids
A Systematic Review, Novel Taxonomy, and Comparative Performance Evaluation
Copyright (c) 2024 Rituraj Rituraj, Várkonyi T. Dániel, Amir Mosavi, Pap József, Várkonyi-Kóczy R. Annamária, Makó Csaba
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Abstract
This article presents a state-of-the-art review of machine learning (ML) methods and applications used in smart grids to predict and optimise energy management. The article discusses the challenges facing smart grids, and how ML can help address them, using a new taxonomy to categorise ML models by method and domain. It describes the different ML techniques used in smart grids as well as examining various smart grid use cases, including demand response, energy forecasting, fault detection, and grid optimisation, and explores how ML can improve these cases. The article proposes a new taxonomy for categorising ML models and evaluates their performance based on accuracy, interpretability, and computational efficiency. Finally, it discusses some of the limitations and challenges of using ML in smart grid applications and attempts to predict future trends. Overall, the article highlights how ML can enable efficient and reliable smart grid systems.
Keywords:
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