An open access journal
Optimizing Energy Systems with Intelligent Solutions
Abstract
This paper explores the optimization of energy systems through intelligent solutions enabled by AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing energy generation, distribution, and consumption. The study highlights the application of AI techniques such as predictive maintenance, energy forecasting, and demand response in improving efficiency, reliability, and sustainability in energy infrastructure. Additionally, it discusses the integration of AI with smart grids, renewable energy sources, and energy storage systems to enable real-time monitoring, grid balancing, and optimization of energy resources. The paper also addresses challenges such as cybersecurity, interoperability, and grid resilience in the adoption of AI-driven engineering solutions in the energy sector. It emphasizes the importance of regulatory support, public-private partnerships, and investment in innovation to harness AI's potential for advancing energy transition and mitigating climate change.
Share and Cite
Article Metrics
References
- Al-Ali, A. R., Nour, M., & El-Hassan, Z. (2018). A review of machine learning and artificial intelligence approaches for the prediction of building energy consumption. Energies, 11(9), 2242.
- Dallinger, D., Sölch, C., Becker, C., & Schlund, S. (2018). Artificial intelligence and machine learning for energy efficiency improvement in buildings: A literature review. Energy and Buildings, 173, 311-322.
- Dong, C., Huang, Y., Qiu, Y., & Zhou, D. (2018). Predictive control for energy management in microgrids: A review. IEEE Transactions on Industrial Informatics, 15(1), 495-503.
- Kusiak, A., Zheng, H., & Song, Z. (2017). Predictive maintenance for renewable energy systems. Renewable Energy, 113, 1303-1312.
- Wang, Z., Qiu, G., & Li, Z. (2019). AI-driven demand response in smart grids: A review. IEEE Transactions on Industrial Informatics, 15(6), 3221-3230.
- Yang, Z., Dong, Z. Y., Li, X., Xie, L., & Wang, X. (2017). Integration of renewable energy sources in smart grid: A review on power electronics interface. Renewable and Sustainable Energy Reviews, 75, 368-379.
- Zhao, Y., & Magoulees, D. (2019). AI and machine learning algorithms for predictive maintenance. In Handbook of Maintenance Management and Engineering (pp. 1005-1029). Springer, Cham.