The Convergence of Machine Learning and Power Systems: Enhancing Efficiency, Optimization, and Sustainability in Energy Management

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@article{1708572,
      author = {Ifeoma Eleweke, Abdulmuiz A. Adekomi, Enita Omuvwie, Adedotun Ademowo, Felix Amakye; Ayoola Olorunnishola},
      title = {The Convergence of Machine Learning and Power Systems: Enhancing Efficiency, Optimization, and Sustainability in Energy Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {1498-1510},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708572.pdf},
      abstract = {The growing demand for efficient, reliable, and sustainable energy solutions has led to the increasing integration of machine learning (ML) in power systems. This study explores the impact of ML on power system management, focusing on three key areas: predictive analytics and forecasting, optimization and control, and fault detection and predictive maintenance. Using Eko Electricity Distribution Company as a case study, the research aims to determine the relationship between ML-driven approaches and improved energy management efficiency, optimization of grid operations, and enhanced fault detection mechanisms. A cross-sectional survey methodology was employed, with data collected from 217 selected employees of the company through self-administered questionnaires. The study utilized Spearman?s Rank Order Correlation to analyze the relationships between ML-driven predictive analytics, optimization, and fault detection with power system management. The results revealed significant positive correlations, with predictive analytics improving energy demand forecasting, optimization techniques enhancing power distribution efficiency, and fault detection systems reducing operational risks by enabling proactive maintenance. The findings emphasize that ML plays a transformative role in modern energy systems by enabling real-time data analysis, reducing transmission losses, and improving grid stability. However, challenges such as data privacy concerns, computational costs, and regulatory barriers hinder widespread adoption. The study concludes that addressing these challenges through interdisciplinary collaboration and the development of standardized AI regulations will facilitate the effective implementation of ML in power systems. Based on these insights, it is recommended that energy providers invest in advanced ML models for demand forecasting, integrate optimization algorithms into smart grid operations, and deploy AI-driven fault detection systems to enhance grid reliability and resilience.},
      keywords = {Machine Learning, Power Systems, Predictive Analytics, Energy Optimization, Fault Detection, Smart Grids, Renewable Energy Integration, Grid Stability, Artificial Intelligence and Energy Management.},
      month = {May},
  }