Smart Energy Management in Nigeria: Implementing IoT and AI for Sustainable Urban Development

Benedict Chibukem Okpala, Christian Chukwuemeka Nzeanorue

Abstract

This article explores the application of Artificial Intelligence (AI) and the Internet of Things (IoT) in improving renewable energy management within urban development in Nigeria. With the global shift towards sustainable living, many urban areas are upgrading their infrastructure to be more environmentally friendly. This transition involves advancements in waste management, increased renewable energy generation, and integration of modern technologies to optimise energy systems. A key focus of this research is the importance of effective policies in addressing challenges within the energy, transportation, and building sectors. The document emphasises collaboration among municipal authorities and stakeholders to optimise clean energy deployment, making cities smarter and more sustainable. The Smart Green Energy (IoT-SGE) system is central to this discussion, as it employs IoT technology for precise energy usage regulation through continuous monitoring and secure communication. The integration of AI allows the system to learn and adapt over time, significantly enhancing energy management efficiency.

The article provides insights into best practices and frameworks for advancing smart cities with improved renewable energy management by examining various studies and practical applications. It also analyses successful case studies from global smart cities, demonstrating how innovative strategies can lead to sustainable urban growth. This research aims to outline a comprehensive strategy for implementing smart energy management systems, fostering a more sustainable future for urban areas in Nigeria.

10.22178/pos.110-4


Keywords


Smart Energy Management; Artificial Intelligence; Internet of Things; Renewable Energy; Sustainable Urban Development; Smart Cities; Energy Efficiency

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References


1. Shakeel, J., Mardani, A., Chofreh, A. G., Goni, F. A., & Klemeš, J. J. (2020). Anatomy of sustainable business model innovation. Journal of Cleaner Production, 261, 121201. doi: 10.1016/j.jclepro.2020.121201

2. Kusakana, K. (2019). Optimal electricity cost minimisation of a grid-interactive Pumped Hydro Storage using ground water in a dynamic electricity pricing environment. Energy Reports, 5, 159–169. doi: 10.1016/j.egyr.2019.01.004

3. Zhou, Y., Chen, M., Tang, Z., & Mei, Z. (2021). Urbanisation, land use change, and carbon emissions: Quantitative assessments for city-level carbon emissions in Beijing-Tianjin-Hebei region. Sustainable Cities and Society, 66, 102701. doi: 10.1016/j.scs.2020.102701

4. Khalid, M. (2024). Smart grids and renewable energy systems: Perspectives and grid integration challenges. Energy Strategy Reviews, 51, 101299. doi: 10.1016/j.esr.2024.101299

5. Karnilius, G. F., Isaac, J. I., & Falama, R. (2024). Smart Grid Technologies: Advancements and Applications in Nigeria. Journal of Multidisciplinary Science: MIKAILALSYS, 2(3), 359–370. doi: 10.58578/mikailalsys.v2i3.3777

6. Ren, Z., Paevere, P., & Chen, D. (2019). Feasibility of off-grid housing under current and future climates. Applied Energy, 241, 196–211. doi: 10.1016/j.apenergy.2019.03.068

7. Khare, V. (2024). Internet of Things in Smart Grid: A Comprehensive Review of Opportunities, Trends, and Challenges. doi: 10.32388/vdpn4q

8. Lin, X., Tang, Y., Ren, J., & Wei, Y. (2021). State of charge estimation with the adaptive unscented Kalman filter based on an accurate equivalent circuit model. Journal of Energy Storage, 41, 102840. doi: 10.1016/j.est.2021.102840

9. Boopathy, P., Liyanage, M., Deepa, N., Velavali, M., Reddy, S., Maddikunta, P. K. R., Khare, N., Gadekallu, T. R., Hwang, W.-J., & Pham, Q.-V. (2024). Deep learning for intelligent demand response and smart grids: A comprehensive survey. Computer Science Review, 51, 100617. doi: 10.1016/j.cosrev.2024.100617

10. Fabbri, K., Gaspari, J., Bartoletti, S., & Antonini, E. (2020). Effect of facade reflectance on outdoor microclimate: An Italian case study. Sustainable Cities and Society, 54, 101984. doi: 10.1016/j.scs.2019.101984

11. Koltsaklis, N. E., Zenginis, I., & Dagoumas, A. S. (2020). Assessing new block order options of the EUPHEMIA algorithm: An optimisation model for the economic dispatch problem in power exchanges. Energy Reports, 6, 3119–3140. doi: 10.1016/j.egyr.2020.11.001

12. Kataray, T., Nitesh, B., Yarram, B., Sinha, S., Cuce, E., Shaik, S., Vigneshwaran, P., & Roy, A. (2023). Integration of smart grid with renewable energy sources: Opportunities and challenges – A comprehensive review. Sustainable Energy Technologies and Assessments, 58, 103363. doi: 10.1016/j.seta.2023.103363

13. Khudhair Abbas, A. (2024). Cybersecurity Challenges in Smart Grids: A Focus on Information Technology. Journal of Electrical Systems, 20(6s), 1394–1407. doi: 10.52783/jes.2922

14. Ang, B. W., & Goh, T. (2018). Bridging the gap between energy-to-GDP ratio and composite energy intensity index. Energy Policy, 119, 105–112. doi: 10.1016/j.enpol.2018.04.038

15. Khare, V. (2024). Internet of Things in Smart Grid: A Comprehensive Review of Opportunities, Trends, and Challenges. doi: 10.32388/vdpn4q

16. Magege, S. R., & Majozi, T. (2021). A comprehensive framework for synthesis and design of heat-integrated batch plants: Consideration of intermittently-available streams. Renewable and Sustainable Energy Reviews, 135, 110125. doi: 10.1016/j.rser.2020.110125

17. Arafat, M. Y., Hossain, M. J., & Alam, M. M. (2024). Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects. Renewable and Sustainable Energy Reviews, 190, 114088. doi: 10.1016/j.rser.2023.114088

18. Cao, L., Li, Y., Zhang, J., Jiang, Y., Han, Y., & Wei, J. (2020). Electrical load prediction of healthcare buildings through single and ensemble learning. Energy Reports, 6, 2751–2767. doi: 10.1016/j.egyr.2020.10.005

19. Nikzad, M., & Samimi, A. (2021). Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems. Applied Energy, 282, 116163. doi: 10.1016/j.apenergy.2020.116163

20. Chijioke Paul Agupugo, Abidemi Obatoyinbo Ajayi, Chinonso Nwanevu, & Segun Samuel Oladipo. (2024). Policy and regulatory framework supporting renewable energy microgrids and energy storage systems. Engineering Science & Technology Journal, 5(8), 2589–2615. doi: 10.51594/estj.v5i8.1460

21. Ehwi, R. J., Holmes, H., Maslova, S., & Burgess, G. (2023). Towards a co-creative stakeholder engagement in Smart City projects: a life-cycle approach. Innovation: The European Journal of Social Science Research, 36(4), 774–799. doi: 10.1080/13511610.2023.2266579


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Copyright (c) 2024 Benedict Chibukem Okpala, Christian Chukwuemeka Nzeanorue

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