AI and 5G Integration for Smart City Energy Systems: A Systematic Literature Review

Smart cities increasingly rely on Artificial Intelligence (AI), 5G, and Internet of Things (IoT) technologies to enhance energy management and support real-time decision-making in smart grids. This study presents a systematic literature review of recent research on the integration of AI and 5G in urban energy systems, with a focus on sustainability goals. It examines how these technologies are used for renewable energy integration, demand-side control, and predictive maintenance across smart environments. Using data from OpenAlex, Scopus, and Web of Science covering the period 2018 to 2025, the review was filtered by language, domain, and scientific relevance. Key findings reveal the use of machine learning models for forecasting, anomaly detection, and system optimization. The review also identifies technical, ethical, and infrastructural challenges, including data heterogeneity, limited interoperability, and regional inequalities in deployment. While AI and 5G offer promising capabilities for real-time monitoring and system automation, the literature shows persistent gaps in algorithm robustness and standardized integration frameworks. The paper emphasizes the need for validated, scalable solutions to achieve long-term energy sustainability. This review provides a clear overview of current trends and future directions in smart energy systems, contributing to a better understanding of how digital technologies shape the future of sustainable urban infrastructures.

@article{Chaymae2025,
  title     = {AI and 5G Integration for Smart City Energy Systems: A Systematic Literature Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {TALBI Chaymae and Rahmouni M’hamed and OUAHBI Younesse and ZITI Soumia},
  doi       = {10.14569/IJACSA.2025.0160880},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160880}
}