Smart Grid Monitoring Using RF Sensor Networks and Intelligent Load Prediction Models in Nigeria
DOI:
https://doi.org/10.54536/ajsts.v5i1.7476Keywords:
IoT, Load Prediction, Machine Learning, Nigeria, Smart GridAbstract
Power system upgrading using smart grid technologies is critical for addressing energy concerns in emerging nations like Nigeria. This work provides a review of smart grid monitoring systems based on Radio Frequency (RF) sensor networks and an intelligent load prediction model. The paper investigates the advancement of RF-enabled monitoring systems, communication protocols, and sensor deployment techniques for real-time data collection in power networks. It evaluates machine learning and artificial intelligence techniques such as regression models, artificial neural networks, and deep learning approaches used for load forecasting. Special emphasis is placed on the obstacles, such as poor communication infrastructure, power outages, and environmental issues of deploying these systems in Nigeria. The paper addresses important research gaps, such as the need for energy-efficient RF systems, strong hybrid prediction models, and scalable smart grid frameworks. Future research directions are suggested to improve the reliability, efficiency, and long-term viability of Nigerian smart grid monitoring systems.
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Copyright (c) 2026 Francis E. Chinda, Abdulmumini Z. Loko, Nyangwarimam O. Ali, Aliyu Muhammad, Timothy A. Shamaki, Salisu M. Lawan, Anas Nasirdeen, Ashafa Abubakar, Muhammad S. Garba
This work is licensed under a Creative Commons Attribution 4.0 International License.
