Optimization of energy grid efficiency with machine learning: A comprehensive review of challenges and opportunities

Samson Kolawole Fasogbon, Ibrahim Ademola Fetuga, Ayodele Temitope Oyeniran, Samuel Adavize Shaibu, Samuel Afolabi, Tochukwu Anthony Ndokwu, Seyi Rufus Oluwadare, John Temitope Onafowokan, Opeyemi Samuel Eso and Victor Blessed Bassey

Renewable and Sustainable Energy Reviews, 2025, vol. 223, issue C

Abstract: Ensuring a sustainable and efficient energy future depends critically on optimizing the electricity grid. Energy grid optimization can be enhanced by machine learning methods that forecast energy needs and supply, optimize energy production and distribution, and identify and stop fraud. Still, there are a number of difficulties in applying machine learning to energy grid optimization. These cover the interpretability and explainability of machine learning models, the ethical and social ramifications of applying machine learning, the absence of standardized datasets and data quality problems, and the integration with the current energy infrastructure and regulatory frameworks. Realizing that attaining a sustainable and efficient energy future requires ongoing research and advancements in machine learning applications for the energy industry. Therefore, this work analyzed the literature already in publication to identify the potential and problems related to machine learning in energy grid optimization and to emphasize the need for ongoing research and development in this area. The paper discovered that developments in machine learning algorithms and techniques, the creation of new datasets and data collecting techniques, and integration of machine learning with other emerging technologies present opportunities for future study in machine learning or energy grid optimization. It also made clear how public-private collaborations and cooperative research are needed.

Keywords: Machine learning; Sustainable energy; Energy grid optimization; Regulatory frameworks; Interpretability (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2025.115980

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