International Journal of Engineering and Modern Technology (IJEMT )

E-ISSN 2504-8848
P-ISSN 2695-2149
VOL. 11 NO. 3 2025
DOI: 10.56201/ijemt.vol.11.no3.


Artificial Intelligence and the Future of Off-Grid Renewable Energy Systems in Nigeria

Justice Mlambo, Email, School of Environmental Science and Engineering, Tianjin University, Dauda Sulaimon Abiola, Awowole Abiodun Olalekan, Okhirebhu Dominion Ikponmosa


Abstract


Artificial Intelligence (AI) has the potential to revolutionize off-grid renewable energy systems in Nigeria by enhancing efficiency, reliability, and sustainability. This study employs a qualitative research approach using secondary data sources to examine the role of AI-driven technologies in optimizing energy generation, distribution, and storage. Anchored in the Technological Innovation System (TIS) theory, the research explores how AI innovations can transform decentralized energy networks while identifying barriers to adoption. Findings indicate that AI- powered predictive analytics enhance energy demand forecasting, reducing inefficiencies and minimizing waste. AI-driven smart grids improve real-time power distribution, and battery management systems optimize storage performance, extending battery lifespan. However, key challenges—including high implementation costs, limited data infrastructure, technical skills shortages, and regulatory constraints—hinder AI adoption in Nigeria’ s energy sector. Case studies from India and Kenya illustrate successful AI applications in off-grid electrification, offering valuable lessons for Nigeria. To facilitate AI integration, policy interventions, public- private partnerships, capacity-building initiatives, and improved digital infrastructure are recommended. By leveraging AI innovations, Nigeria can advance rural electrification, promote sustainable energy access, and drive economic development.


keywords:

Artificial intelligence, Off-grid energy, Renewable energy, Smart grids, Nigeria


References:


Khosravi, A.; Syri, S.; Pabon, J.J.G.; Sandoval, O.R.; Caetano, B.C.; Barrientos, M.H. Energy
modeling of a solar dish/Stirling by artificial intelligence approach. Energy Convers.
Manag. 2019, 199, 112021, doi:10.1016/j.enconman.2019.112021.
Morley, J.; Machado, C.C.V.; Burr, C.; Cowls, J.; Joshi, I.; Taddeo, M.; Floridi, L. The ethics of
AI in health care: A mapping review. Soc. Sci. Med. 2020, 260, 113172.
van Wynsberghe, A. Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics
2021, 1, 3, doi:10.1007/s43681-021-00043-6.
Floridi, L.; Cowls, J.; Beltrametti, M.; Chatila, R.; Chazerand, P.; Dignum, V.; Luetge, C.;
Madelin, R.; Pagallo, U.; Rossi, F.; et al. AI4People—
An Ethical Framework for a Good
AI Society: Opportunities, Risks, Principles, and Recommendations. Minds Mach. 2018,
28, 689– 707, doi:10.1007/s11023-018- 9482-5.
Floridi, L.; Cowls, J.; King, T.C.; Taddeo, M. How to Design AI for Social Good: Seven
Essential Factors. Sci. Eng. Ethics 2020, 26, 1771– 1796, doi:10.1007/s11948-020-
00213-5.
Vinuesa, R.; Azizpour, H.; Leite, I.; Balaam, M.; Dignum, V.; Domisch, S.; Felländer, A.;
Langhans, S.D.; Tegmark, M.; Fuso Nerini, F. The role of artificial intelligence in
achieving the Sustainable Development Goals. Nat. Commun. 2020, 11, 1– 10.
Jha, S.K.; Bilalovic, J.; Jha, A.; Patel, N.; Zhang, H. Renewable energy: Present research and
future scope of Artificial Intelligence. Renew. Sustain. Energy Rev. 2017, 77, 297– 317.
Ahmad, T.; Zhang, D.; Huang, C.; Zhang, H.; Dai, N.; Song, Y.; Chen, H. Artificial
intelligence in sustainable energy industry: Status Quo, challenges and opportunities. J.
Clean. Prod. 2021, 289, 125834.
Holden, E.; Linnerud, K.; Rygg, B.J. A review of dominant sustainable energy narratives.
Renew. Sustain. Energy Rev. 2021, 144, 110955.
Kabeyi, M. J. B., and A. O. Oludolapo. 2021b. “ Preliminary Design of a Bagasse Based
Firm Power Plant for a Sugar Factory.” Presented at the South African
Universities Power Engineering Conference (SAUPEC), Nortn West University, South
Africa, 27-28 January 2021, 104. [Online]. https://ieeexplore.ieee.org/stamp/stamp.jsp?
arnumber = 9377242.
Kabeyi, M., and O. Olanrewaju. 2021c. “ Performance Analysis of a Sugarcane Bagasse
Cogeneration Power Plant in Grid Electricity Generation.” Presented at the 11th
Annual International Conference on Industrial Engineering and Operations
Management Singapore, March 7– 11, 2021. [Online]. http://www.ieomsociety.org/
singapore2021/papers/201.pdf
Kabeyi, M. J. B., and O. A. Olanrewaju. 2022g. “ Geothermal Wellhead Technology Power
Plants in Grid Electricity Generation: A Review.” Energy Strategy Reviews 39 (January
2022): 100735, 2022/01/01. doi:10.1016/j.esr.2021. 100735.
Kabeyi, M. J. B., and O. A. Olanrewaju. 2021a. “ The Relationship Between Electricity
Consumption and Economic Development.” Presented at the International
Conference on Electrical, Computer and Energy Technologies (ICECET), Cape
Town,
South
Africa,
9-10
December
[Online].
http://www.icecet.com/submission/268.
Bhattarai, T. N., S. Ghimire, B. Mainali, S. Gorjian, H. Treichel, and S. R. Paudel. 2022.
“ Applications of Smart Grid Technology in Nepal: Status, Challenges, and
Opportunities.” Environmental Science and Pollution Research, 2022/02/09.
https://doi.org/10.1007/s11356-022-19084-3.
Kabeyi, M. J. B., and A. O. Olanrewaju. 2022d. “ The Use of Smart Grids in the Energy
Transition.” In 2022 30th Southern African Universities Power Engineering
Conference (SAUPEC), 25– 27 Jan. 2022: IEEE, pp. 1– 8. [Online].
https://ieeexplore.ieee.org/abstract/document/9730635.
https://doi.org/10.1109/SAUPEC55179.2022. 9730635.
Particle Industries. 2022. “ How IoT Enables the Smart Grid - Applications, Benefits, and Use
Cases.” https://www. particle.io/iot-guides-and-resources/iot-smart-grid-applications-
benefits-and-use-cases/ (accessed 15 October 2022).
Kabeyi, M. J. B., and O. A. Olanrewaju. 2022a. “ Sustainable Energy Transition for
Renewable and Low Carbon Grid Electricity Generation and Supply (in English).”
Frontiers in Energy Research, Review 9 (743114): 1– 45, 2022- March-24.
doi:10.3389/fenrg.2021.743114.
Kabeyi, M. J. B., and O. A. Olanrewaju. 2022b. “ Relationship Between Electricity
Consumption and Economic Development.” Presented at the International
Conference on Electrical, Computer and Energy Technologies (ICECET), Cape
Town-South
Africa,
9– 10
December
[Online].
https://ieeexplore.ieee.org/stamp/stamp. jsp?tp = &arnumber = 9698413.
Kabeyi, M. J. B., and O. A. Olanrewaju. 2022c. “ Biogas Production and Applications in the
Sustainable Energy Transition.” Journal of Energy 43: 8750221, 2022/07/09.
doi:10.1155/2022/8750221.
Kabeyi, M. J. B., and A. O. Olanrewaju. 2022d. “ The Use of Smart Grids in the Energy
Transition.” In 2022 30th Southern African Universities Power Engineering
Conference (SAUPEC), 25– 27 Jan. 2022: IEEE, pp. 1– 8. [Online].
https://ieeexplore.ieee.org/abstract/document/9730635.
https://doi.org/10.1109/SAUPEC55179.2022. 9730635
Kabeyi, M. J. B., and O. A. Olanrewaju. 2022h. “ Sugarcane Molasses to Energy Conversion for
Sustainable Production and Energy Transition,” Presented at the 12th Annual
Istanbul International Conference on Industrial Engineering and Operations
Management, Istanbul, Turkey, March 7– 10, 2022, 405. [Online]. https://
ieomsociety.org/proceedings/2022istanbul/405.pdf.
Gilan, S.S.; Dilkina, B. Sustainable Building Design: A Challenge at the Intersection of
Machine Learning and Design Optimization; 2015; [30] Vázquez, F.I.; Kastner, W.
Usage profiles for sustainable buildings. In Proceedings of the Proceedings of the 15th
IEEE International Conference on Emerging Technologies and Factory Automation,
ETFA 2010; 2010.
Gilner, E.; Galuszka, A.; Grychowski, T. Application of artificial intelligence in sustainable
building design - Optimisation methods. In Proceedings of the 2019 24th
International Conference on Methods and Models in Automation and Robotics, MMAR
2019; Institute of Electrical and Electronics Engineers Inc., 2019; pp. 81– 86.
Iddianozie, C.; Palmes, P. Towards smart sustainable cities: Addressing semantic
heterogeneity in Building Management Systems using discriminative models. Sustain.
Cities Soc. 2020, 62, 102367, doi:10.1016/j.scs.2020.102367.
Kadar, T.; Kadar, M. Sustainability Is Not Enough: Towards AI Supported Regenerative
Design. In Proceedings of the Proceedings - 2020 IEEE International Conference
on Engineering, Technology and Innovation, ICE/ITMC 2020; Institute of Electrical
and Electronics Engineers Inc., 2020.
Santos, C.; Ferreira, J.C.; Rato, V.; Resende, R. Public building energy efficiency - An IoT
approach. In Proceedings of the Advances in Intelligent Systems and Computing;
Springer Verlag, 2019; Vol. 806, pp. 65– 72
Juan, Y.K.; Gao, P.; Wang, J. A hybrid decision support system for sustainable office
building renovation and energy performance improvement. Energy Build. 2010, 42,
290– 297, doi:10.1016/j.enbuild.2009.09.006.
Silva, F.; Analide, C.; Rosa, L.; Felgueiras, G.; Pimenta, C. Ambient Sensorization for the
Furtherance of Sustainability. In Proceedings of the Advances in Intelligent
Systems and Computing; Springer Verlag, 2013; Vol. 219, pp. 179– 186.
Mattiussi, A.; Rosano, M.; Simeoni, P. A decision support system for sustainable energy supply
combining multi-objective and multi-attribute analysis: An


DOWNLOAD PDF

Back


Google Scholar logo
Crossref logo
ResearchGate logo
Open Access logo
Google logo