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With an emphasis on improving knowledge and management of energy resources, this comprehensive review examines the various applications of machine learning (ML) in energy systems. The study investigates cutting-edge methods and technology to provide insights into the growing need to switch to greener and more efficient energy systems. It encompasses various ML methods for modeling energy systems, such as optimization, decision-making processes, and predictive analytics. The review provides a comprehensive picture of the benefits and difficulties of ML in energy resource optimization by combining the results of several investigations. The consequences for efficiency, sustainability, and the general shift to cleaner energy systems are also covered in the paper.

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Towards Sustainable Energy Futures: A Review of Machine Learning Applications in Energy Systems

  • 会议论文

  • 发表日期：

    2025年

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  • Rupinder Kaur,
  • Raman Kumar,
  • Himanshu Aggarwal

摘要

With an emphasis on improving knowledge and management of energy resources, this comprehensive review examines the various applications of machine learning (ML) in energy systems. The study investigates cutting-edge methods and technology to provide insights into the growing need to switch to greener and more efficient energy systems. It encompasses various ML methods for modeling energy systems, such as optimization, decision-making processes, and predictive analytics. The review provides a comprehensive picture of the benefits and difficulties of ML in energy resource optimization by combining the results of several investigations. The consequences for efficiency, sustainability, and the general shift to cleaner energy systems are also covered in the paper.