Real-Time Energy Demand Forecasting and Adaptive Demand Response Optimization for IoT-Enabled Smart Grids

Authors

  • Aliyu Musa Kida Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Nigeria
  • Muhammed Zaharadeen Ahmed International Islamic University Malaysia
  • Abdulkadir Hamidu Alkali Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Nigeria
  • Jafaru Usman Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Nigeria
  • Aisha Hassan Abdalla Hashim Department of Electrical and Electronic Engineering Science, University of Johannesburg, South Africa.

DOI:

https://doi.org/10.26740/vubeta.v2i2.36818

Keywords:

Energy, Reliability, load, Efficiency, cost

Abstract

The evolution of energy systems concerning IoT-enabled smart grids require new innovative solutions to address enormous open issues in demand-supply balance, grid reliability, and sustainability. In this research work, attention is centered on integrating real-time energy demand forecast and adaptive demand response optimization. This is solely to improve efficiency and resilience of modern smart grids. We use Advanced ML technique known as Long Short-Term Memory (LSTM) networks to determine accurate energy demand forecast by capturing temporal dependencies and non-linear trends when consuming energy data. Using Simulation, we present model’s efficacy in achieving accurate forecast using Mean Absolute Percentage Error (MAPE) of 5.6%, a peak load reduction of 20%, and energy cost savings that exceeds 24%. We validate Computational efficiency with execution times that is better for real-time operation and grid scalability of 10,000 IoT devices. these results pave way for future research in hybrid forecast analysis, and multi-objective optimization. This can ensure stability of the grid in dynamic and decentralized energy landscape

Author Biographies

Aliyu Musa Kida, Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Nigeria

Aliyu Musa Kida is a lecturer in the Department of Computer Engineering, Maiduguri Nigeria.
He is received the BSc and MSc in England in Cybersecurity department. He is currently a Full
time PhD student in the University of Maiduguri Nigeria. His research interest is mainly in
smart grids and renewable energies. He can be contacted at email: aliyukida@gmail.com

Muhammed Zaharadeen Ahmed, International Islamic University Malaysia

Dr. Muhammed Zaharadeen is a lecturer in the Department of Computer Engineering,
Maiduguri Nigeria and a postdoctoral research fellow in the International Islamic University
Malaysia. His PhD is centered on mobilty management in both IP and NDN networks. He is
actively involve in research for blockchain security and smart grids. He can be contacted at
email: zaharadeen22@unimaid.edu.ng

Abdulkadir Hamidu Alkali, Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Nigeria

Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Nigeria

Jafaru Usman, Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Nigeria

Department of Computer Engineering, Faculty of Engineering, University of Maiduguri, Nigeria

Aisha Hassan Abdalla Hashim, Department of Electrical and Electronic Engineering Science, University of Johannesburg, South Africa.

Department of Electrical and Electronic Engineering Science, University of Johannesburg, South Africa.

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Published

2025-06-20

How to Cite

[1]
Aliyu Musa Kid, M. Z. Ahmed, Abdulkadir Hamidu Alkali, Jafaru Usman, and Aisha Hassan Abdalla Hashim, “Real-Time Energy Demand Forecasting and Adaptive Demand Response Optimization for IoT-Enabled Smart Grids”, Vokasi Unesa Bull. Eng. Technol. Appl. Sci., vol. 2, no. 2, pp. 366–375, Jun. 2025.

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Vol. 2 No. 2 (2025)

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Copyright (c) 2025 Aliyu Musa Kida, Muhammed Zaharadeen Ahmed, Abdulkadir Hamidu Alkali, Jafaru Usman, Aisha Hassan Abdalla Hashim

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