Timothy Ozioma Nwokorie1, Moses Adondua Abah2✉, Micheal Abimbola Oladosu2, Ochuele Dominic Agida2, Obojobo Donatus Obukeajeta3 and Christian Onyemaechi Asogwa4
1Department of Electronic and Computer Engineering, Faculty of Engineering, Lagos State University, Lagos State, Nigeria
2ResearchHub Nexus Institute, Nigeria
3Department of Mechanical Engineering, Faculty of Engineering, Southern Delta University, Ozoro, Nigeria
4Department of Electronics and Computer Engineering, Faculty of Engineering, University of Nigeria, Nsukka, Enugu State, Nigeria
Received: April 25, 2026/ Revised: May 27, 2026/Accepted: May 29, 2026
(✉) Corresponding Author:: m.abah@fuwukari.edu.ng
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in electrical power systems has transformed the way energy is generated, transmitted, and distributed. AI and ML techniques enable predictive analytics, fault detection, and optimization of power system operations. With the increasing complexity and decentralization of power systems, AI and ML play a crucial role in ensuring reliability, efficiency, and sustainability. This review explores the current applications of AI and ML in electrical power systems, including load forecasting, renewable energy integration, grid management, and cybersecurity. It also discusses future directions, challenges, and opportunities for AI-driven innovation in the energy sector. The review employed a comprehensive methodology, involving a literature review of AI and ML applications in power systems, analysis of case studies, evaluation of techniques (machine learning and deep learning models), and examination of applications (grid stability assessment, predictive maintenance, fault diagnosis, and renewable energy integration). Findings from this study revealed that AI and ML have transformative potential in electrical power systems, enhancing efficiency, reliability, and sustainability. Key findings include: AI-driven predictive analytics and fault detection improve grid stability and reduce downtime; ML models optimize renewable energy output and integration; and AI-powered control systems enhance grid management. The review also identified challenges, such as data quality and cybersecurity concerns, and highlighted future research directions, including developing more advanced AI algorithms and integrating AI with emerging technologies like IoT and blockchain. The integration of AI and ML in electrical power systems has transformative potential, enhancing efficiency, reliability, and sustainability. While challenges exist, ongoing research and development can address these issues. As the energy sector evolves, AI and ML will play a crucial role in shaping its future, driving innovation and improvement. Overall, AI and ML can revolutionize power system operations, but require careful implementation and ongoing research.
Keywords: Machine learning, Artificial intelligence, Energy, Sustainability, Blockchain, and Power systems
References
Afridi, Y. S., Ahmad, K., & Hassan, L. (2021). Artificial intelligence based prognostic maintenance of renewable energy systems: A review of techniques, challenges, and future research directions. Renewable and Sustainable Energy Reviews, 148, Article 111254. https://doi.org/10.1016/j.rser.2021.111254
Alsaigh, R., Mehmood, R., & Katib, I. (2022). AI explainability and governance in smart energy systems: A review. Sensors, 22(21), Article 8670. https://doi.org/10.3390/s22218670
Boukhris, A., Krichen, L., & Bacha, S. (2023). Artificial intelligence-based short-term load forecasting methods for smart grid applications: A review. Energies, 16(15), Article 5670. https://doi.org/10.3390/en16155670
Bouqueta, P., Jackson, I., Nick, M., & Kaboli, A. (2023). AI-based forecasting for optimised solar energy management and smart grid efficiency. International Journal of Production Research, 61(15), 4623–4644. https://doi.org/10.1080/00207543.2023.2269565
Chen, C., Fu, H., Zheng, Y., Tao, F., & Liu, Y. (2023). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, 581–594. https://doi.org/10.1016/j.jmsy.2023.10.010
Dudek, G., Piotrowski, P., & Baczyński, D. (2023). Intelligent forecasting and optimization in electrical power systems: Advances in models and applications. Energies, 16(7), Article 3024. https://doi.org/10.3390/en16073024
Errandonea, I., Beltrán, S., & Arrizabalaga, S. (2020). Digital twin for maintenance: A literature review. Computers in Industry, 123, Article 103316. https://doi.org/10.1016/j.compind.2020.103316
Gao, S., Wang, W., Chen, J., Wu, X., & Shao, J. (2024). Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster. Global Energy Interconnection, 7(3), 336–346. https://doi.org/10.1016/j.gloei.2024.06.005
Ghorbanian, V., Yazdani, A., Askarany, D., & Moghaddam, M. (2021). IoT-integrated modern power systems: A survey of machine learning applications. The Electricity Journal, 34(1), Article 106879. https://doi.org/10.1016/j.tej.2020.106879
Helal, M. A. R., Li, W., & Yu, J. (2022). Applications of Artificial Intelligence in Smart Grids: A comprehensive review. Energies, 15(18), Article 6621. https://doi.org/10.3390/en15186621
Heluany, J. B., & Gkioulos, V. (2024). A review on digital twins for power generation and distribution. International Journal of Information Security, 23, 1171–1195. https://doi.org/10.1007/s10207-023-00784-x
Henao, F., Edgell, R., Sharma, A., & Olney, J. (2025). AI in power systems: A systematic review of key matters of concern. Energy Informatics, 8, Article 76. https://doi.org/10.1186/s42162-025-00529-1
Hossain, R. R., & Kumar, R. (2023). Machine learning accelerated real-time model predictive control for power systems. IEEE/CAA Journal of Automatica Sinica, 10(4), 916–930. https://doi.org/10.1109/JAS.2023.123135
Inturi, V., Ghosh, B., Rajasekharan, S. G., & Pakrashi, V. (2024). A review of digital twinning for rotating machinery. Sensors, 24(15), Article 5002. https://doi.org/10.3390/s24155002
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–52. https://doi.org/10.1016/j.cirpj.2020.02.002
Kuyumani, E. M., Hasan, A. N., & Shongwe, T. (2023). A hybrid model based on CNN-LSTM to detect and forecast harmonics: A case study of an Eskom substation in South Africa. Electric Power Components and Systems, 51(8), 746–760. https://doi.org/10.1080/15325008.2023.2181883
Li, Y., Ding, Y., He, S., Hu, F., Duan, J., Wen, G., Geng, H., Wu, Z., Gooi, H. B., Zhao, Y., Zhang, C., Mei, S., & Zeng, Z. (2024). Artificial intelligence–based methods for renewable power system operation. Nature Reviews Electrical Engineering, 1(3), 163–179. https://doi.org/10.1038/s44287-024-00018-9
Mchirgui, N., Quadar, N., Kraiem, H., & Lakhssassi, A. (2024). The applications and challenges of Digital Twin technology in smart grids: A comprehensive review. Applied Sciences, 14(23), Article 10933. https://doi.org/10.3390/app142310933
Oelhaf, J., Kordowich, G., Pashaei, M., Bergler, C., Maier, A., Jäger, J., & Bayer, S. (2025). A scoping review of machine learning applications in power system protection and disturbance management. International Journal of Electrical Power & Energy Systems, 172, Article 111257. https://doi.org/10.1016/j.ijepes.2025.111257
Pandey, U., Pathak, A., Kumar, A., & Mondal, S. (2023). Applications of artificial intelligence in power system operation, control, and planning: A review. Clean Energy, 7(6), 1199–1218. https://doi.org/10.1093/ce/zkad061
Rahmani‑Sane, G., Azad, S., & Ameli, M. T. (2025). The applications of artificial intelligence and digital twin in power systems: An in‑depth review. IEEE Access, 13, 108573–108608. https://doi.org/10.1109/ACCESS.2025.3580340
Shen, Z., Arraño‑Vargas, F., & Konstantinou, G. (2023). Artificial intelligence and digital twins in power systems: Trends, synergies and opportunities. Digital Twin, 1(3), Article 11. https://doi.org/10.12688/digitaltwin.17632.2
Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023). Prospects and challenges of the machine learning and data-driven methods for the predictive analysis of power systems: A review. Energies, 16(10), Article 4025. https://doi.org/10.3390/en16104025
Su, T., & Zhao, J. (2025). Grid-enhancing technologies for clean energy systems. Nature Reviews Clean Technology, 1, 16–31. https://doi.org/10.1038/s44359-024-00001-5
Trull, O., García‑Díaz, J. C., & Peiró‑Signes, A. (2022). Multiple seasonal electric load forecast method based on improved sequence-to-sequence GRU with adaptive temporal dependence. International Journal of Electrical Power & Energy Systems, 137, Article 107777. https://doi.org/10.1016/j.ijepes.2022.107777
Ukoba, K., Olatunji, K. O., Adeoye, E., Jen, T.-C., & Madyira, D. M. (2024). Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Journal of Engineering in Industry and Commerce, Advance online publication. https://doi.org/10.1177/0958305X241256293
Wang, Z. J., Wei, W., Pang, J. Z. F., Liu, F., Yang, B., Guan, X. P., & Mei, S. W. (2023). Online optimization in power systems with high penetration of renewable generation: Advances and prospects. IEEE/CAA Journal of Automatica Sinica, 10(4), 839–858. https://doi.org/10.1109/JAS.2023.123126
Zhang, D., Jin, X., Shi, P., & Chew, X. C. (2023). Real-time load forecasting model for the smart grid using Bayesian optimized CNN-BiLSTM. Frontiers in Energy Research, 11, Article 1193662. https://doi.org/10.3389/fenrg.2023.1193662
Zheng, H., Paiva, A. R., & Gurciullo, C. S. (2020). Advancing from predictive maintenance to intelligent maintenance with AI and IIoT. Annual Reviews in Control, 50, 394–406. https://doi.org/10.1016/j.arcontrol.2020.10.010
How to cite this article
Nwokorie, T. O., Abah, M. A., Oladosu, M. A., Agida, O. D., Obukeajeta, O. D., & Asogwa, C. O. (2025). Artificial intelligence and machine learning in electrical power systems: A review of applications and future directions. Science Archives, 7(2), 41–52. https://doi.org/10.47587/SA.2026.7203
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