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AI for Sustainable Energy Management in Smart Cities in Nigeria


Authors : Stella Ebere Edeh; Chinagolum Ituma; Maduabuchi Ignatius Edeh; Njoku Chimee Mercy

Volume/Issue : Volume 11 - 2026, Issue 8 - August


Google Scholar : https://tinyurl.com/ynfzxjun

Scribd : https://tinyurl.com/yd9whpk8

DOI : https://doi.org/10.38124/ijisrt/26aug235

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : This study explores the role of artificial intelligence driven smart energy management system as a tool for addressing rising urban energy demand and promoting sustainable development in developing countries, with specific focus on Nigeria. The study revealed that rapid urbanization has placed significant strain on conventional power infrastructure, resulting in inefficiencies, high operational costs, frequent outages, and increased carbon emissions. To respond to these challenges, the paper examined the design and application of an AI-powered platform that integrates data from IoT sensors, smart meters, and weather stations to support real time energy monitoring and decision making. The study analyzed existing utility operations and technical frameworks to identify key system requirements and data workflows that inform the development of an intelligent energy management solution. Using Objection-Oriented Analysis and Design Methodology (OOADM) and Unified Modeling Language (UML), the study proposed modular and scalable system architecture capable of demand forecasting, grid anomaly detection, predictive maintenance, and optimized integration of renewable energy sources such as solar and wind. The findings indicated that the adoption of AI-driven energy management systems can substantially reduce peak energy load, minimize unplanned outages, lower maintenance costs, and cut carbon emissions, while providing grid operators and policymakers with accurate, real time insights. The paper concludes that intelligent energy management solutions are critical for improving efficiency, strengthening energy security, and supporting sustainable national development in Nigeria and similar developing economies.

Keywords : Energy Management System, Smart Grid, Renewable Energy Integration, Internet of Thing.

References :

  1. United Nations, “World urbanization prospects 2022: Urbanization and energy demand,” UN Rep., 2022.
  2. International Energy Agency, “The future of smart grids and AI applications in energy management,” IEA Rep., 2021.
  3. L. Zhang, W. Chen, and H. Zhao, “Machine learning applications in sustainable energy management,” Energy Informatics, vol. 14, no. 3, pp. 88–103, 2020.
  4. J. Wang, Y. Zhang, and X. Li, “Big data and AI in smart grid energy optimization,” J. Energy Syst., vol. 22, no. 1, pp. 112–129, 2022.
  5. X. Liu, Y. Zhang, and L. Wang, “Intelligent fault detection in power grids using AI-based analytics,” Smart Grid Technol. J., vol. 17, no. 2, pp. 102–120, 2021.
  6. A. Gharaibeh, M. Salahuddin, and M. Hussain, “Smart cities and AI-driven energy management systems,” J. Smart Infrastructure, vol. 12, no. 2, pp. 55–72, 2020.
  7. V. Sharma and R. Kaushik, “AI-driven demand forecasting for smart city energy grids,” J. Smart Grid Technol., vol. 15, no. 3, pp. 67–84, 2021.
  8. Y. Sun, H. Wang, and G. Li, “AI-based predictive maintenance for power grid equipment,” IEEE Trans. Ind. Informatics, vol. 17, no. 3, pp. 1658–1667, 2021.
  9. J. Chen, W. Li, and Y. Wu, “AI-driven energy storage optimization in smart grids,” IEEE Trans. Smart Grid, vol. 12, no. 3, pp. 2345–2356, 2021.
  10. A. Mohamed, A. Hasan, and M. Khan, “AI-based home energy management systems: A review,” Renew. Sustain. Energy Rev., vol. 115, p. 109389, 2020.
  11. K. Rahman, T. Farid, and Z. Ahmed, “Industrial energy management using AI: Challenges and best practices,” J. Sustainable Industry, vol. 18, no. 1, pp. 134–151, 2021.
  12. S. Fathi, A. Rahimi, and N. Jafari, “AI-driven HVAC optimization for energy-efficient buildings,” Sustain. Energy Archit., vol. 14, no. 1, pp. 88–103, 2022
  13. C. Eze, C. Nwankwo, and O. Okoro, “Predictive maintenance in Nigeria's power sector using AI techniques,” Int. J. Electr. Power Energy Syst., vol. 134, p. 107234, 2022.
  14. O. Adeyemi, R. Bello, and T. Ajayi, “Renewable energy policies in Nigeria: An assessment of the National Renewable Energy and Energy Efficiency Policy (NREEEP),” Energy Policy Rev., vol. 20, no. 1, pp. 34–52, 2023.
  15. C. Ugwoke, O. Nnamdi, and L. Chukwu, “Transmission losses and grid instability in Nigeria: A policy review,” Nigerian J. Electr. Eng., vol. 16, no. 1, pp. 89–107, 2023.
  16. A. Ibrahim, E. Okon, and U. Eze, “Microgrid solutions for decentralized energy systems in Nigeria,” African J. Energy Syst., vol. 9, no. 3, pp. 203–220, 2023.
  17. P. Okoro, Y. Hassan, and A. Yusuf, “Blockchain-integrated AI platforms for decentralized energy trading in Nigeria,” African J. Renewable Energy, vol. 12, no. 3, pp. 145–162, 2023.
  18. E. Hossain, M. Farooq, and T. Rahman, “Distributed energy resources and AI-based optimization techniques,” Int. J. Smart Grids, vol. 11, no. 2, pp. 78–96, 2021.
  19. R. Yildiz, B. Ozdemir, and T. Can, “AI-driven renewable energy integration: Opportunities and challenges,” Renewable Energy J., vol. 23, no. 2, pp. 155–172, 2022.
  20. M. Poyyamozhi et al., “IoT: A promising solution to energy management in smart buildings: A systematic review, applications, barriers, and future scope,” Buildings, vol. 14, no. 11, p. 3446, 2024. doi: 10.3390/buildings14113446
  21. F. Ali, R. Khan, and Z. Malik, “AI-driven optimization of solar and wind energy integration: Challenges and solutions,” Renewable Energy Adv., vol. 9, no. 4, pp. 250–267, 2021.
  22. S. Patel and T. Wang, “AI-enabled demand-side management for energy efficiency improvement,” Int. J. Energy Optim., vol. 21, no. 2, pp. 201–220, 2022.

This study explores the role of artificial intelligence driven smart energy management system as a tool for addressing rising urban energy demand and promoting sustainable development in developing countries, with specific focus on Nigeria. The study revealed that rapid urbanization has placed significant strain on conventional power infrastructure, resulting in inefficiencies, high operational costs, frequent outages, and increased carbon emissions. To respond to these challenges, the paper examined the design and application of an AI-powered platform that integrates data from IoT sensors, smart meters, and weather stations to support real time energy monitoring and decision making. The study analyzed existing utility operations and technical frameworks to identify key system requirements and data workflows that inform the development of an intelligent energy management solution. Using Objection-Oriented Analysis and Design Methodology (OOADM) and Unified Modeling Language (UML), the study proposed modular and scalable system architecture capable of demand forecasting, grid anomaly detection, predictive maintenance, and optimized integration of renewable energy sources such as solar and wind. The findings indicated that the adoption of AI-driven energy management systems can substantially reduce peak energy load, minimize unplanned outages, lower maintenance costs, and cut carbon emissions, while providing grid operators and policymakers with accurate, real time insights. The paper concludes that intelligent energy management solutions are critical for improving efficiency, strengthening energy security, and supporting sustainable national development in Nigeria and similar developing economies.

Keywords : Energy Management System, Smart Grid, Renewable Energy Integration, Internet of Thing.

Paper Submission Last Date
31 - August - 2026

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