IoT and AI for Smart Grid Optimization in Urban Areas
DOI:
https://doi.org/10.22105/scfa.v2i1.43Keywords:
Smart grid, Internet of things, Artificial intelligence, Urban optimization, Energy efficiencyAbstract
As cities grow and the need for energy rises, optimizing power distribution and enhancing grid efficiency becomes vital. This study explores how the integration of the Internet of Things (IoT) and Artificial Intelligence (AI) can be utilized to improve smart grid systems in urban areas. This strategy aims to enhance energy distribution, cut down on inefficiencies, and lessen power outages by using IoT devices for immediate data gathering and AI-powered predictive algorithms for informed decision-making. Additionally, the system's capability to identify irregularities and forecast equipment failures aids in lowering maintenance expenses. Simulation results from a model urban grid indicate that smart grid solutions driven by AI and IoT can result in superior energy management, greater grid reliability, and promote more sustainable development in urban settings.
References
[1] Jaradat, M., Jarrah, M., Bousselham, A., Jararweh, Y., & Al-Ayyoub, M. (2015). The internet of energy: smart sensor networks and big data management for smart grid. Procedia computer science, 56, 592–597. https://doi.org/10.1016/j.procs.2015.07.250
[2] Muhammad, A., Ahmad Ishaq, A., Igbinovia, O., & Idris, M. (2023). Artificial intelligence and machine learning for real-time energy demand response and load management. Journal of technology innovations and energy, 2(2), 20–29. http://dx.doi.org/10.56556/jtie.v2i2.537
[3] Elhabyb, K., Baina, A., Bellafkih, M., & Deifalla, A. F. (2024). Machine learning algorithms for predicting energy consumption in educational buildings. International journal of energy research, 2024(1), 6812425. https://doi.org/10.1155/2024/6812425
[4] Arunkumar, G. (2024). AI-based predictive maintenance strategies for electrical equipment and power networks. International journal of artificial intelligence in electrical engineering (IJAIEE), 2(1), 1–13. https://iaeme.com/Home/issue/IJAIEE?Volume=2&Issue=1
[5] Devi, E. M. R., Shanthakumari, R., Dhanushya, S., & Kiruthika, G. (2024). AI models for predictive maintenance. In data analytics and artificial intelligence for predictive maintenance in smart manufacturing (pp. 69–94). CRC Press. https://doi.org/10.1201/9781003480860
[6] Chou, J. S., & Tran, D. S. (2018). Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy, 165, 709–726. https://doi.org/10.1016/j.energy.2018.09.144
[7] Alahi, M. E. E., Sukkuea, A., Tina, F. W., Nag, A., Kurdthongmee, W., Suwannarat, K., & Mukhopadhyay, S. C. (2023). Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23(11), 5206. https://doi.org/10.3390/s23115206
[8] Mohapatra, H., & Rath, A. K. (2020). Fault-tolerant mechanism for wireless sensor network. IET wireless sensor systems, 10(1), 23–30. https://doi.org/10.1049/iet-wss.2019.0106
[9] Arévalo, P., & Jurado, F. (2024). Impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. Energies, 17(17), 4501. https://doi.org/10.3390/en17174501
[10] Mohapatra, H. (2021). Smart city with wireless sensor network: networking of smart city applications. Kindle. https://B2n.ir/ew1351
Metrics
