AI-Driven Optimization of Renewable Energy Grids Enhancing Efficiency and Sustainability

Authors

  • Mohamed Belrzaeg Department of Energy Systems Engineering, Karabuk University, Karabuk, Turkey Author
  • Abdussalam Ali Ahmed Mechanical Engineering Department, Bani Waleed University, Bani Waleed, Libya Author
  • Ahmed Salem Daw Alarga Electrical Engineering Department, Elmergib University, Khums, Libya Author

Keywords:

Artificial Intelligence, Renewable Energy Grids, Energy Optimization, Sustainability, Energy Forecasting,, Machine Learning, Energy Storage, Demand Response

Abstract

The integration of renewable energy sources into existing power grids presents significant challenges due to the variability and unpredictability of sources like wind and solar. This paper investigates the role of Artificial Intelligence (AI) in optimizing renewable energy grids to enhance their efficiency and sustainability. By leveraging machine learning, predictive analytics, and real-time data processing, AI offers transformative potential in overcoming the challenges of renewable energy management. This research reviews current AI applications in energy forecasting, storage optimization, and demand response, highlighting both the benefits and obstacles. The findings underscore the importance of AI in achieving a more resilient and sustainable energy future, while also identifying areas for future research.

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Published

2025-01-16

Issue

Volume 1, No. 1, 2025

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mohamed Belrzaeg, Abdussalam Ali Ahmed, & Ahmed Salem Daw Alarga. (2025). AI-Driven Optimization of Renewable Energy Grids Enhancing Efficiency and Sustainability. The Open European Journal of Applied Sciences (OEJAS), 1(1), 24-35. https://easdjournals.com/index.php/oejas/article/view/15