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Nazar, Mustafa
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Swarm Intelligence Algorithms for Resource Allocation in Renewable-Powered Smart City Infrastructures Nazar, Mustafa; Majeed, Adil Abbas; Abdul Radhi, Rafah Hassan; Jafar, Qusay Mohammed; Khalil, Baker Mohammed; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1355

Abstract

The increasing integration of renewable energy sources into urban systems necessitates the development of intelligent resource management strategies to ensure optimal and reliable power distribution. Swarm Intelligence (SI) algorithms have emerged as a promising solution for addressing the complex energy management challenges inherent in smart cities, such as generation variability, distributed loads, and the need for real-time decision-making. This paper conducts a rigorous comparative analysis of three prominent SI algorithms—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC)—within a simulated, renewable-powered smart city environment. Our model incorporates edge computing nodes, solar and wind generation systems, and heterogeneous urban load profiles, including residential, municipal, and electric vehicle charging demands. The study evaluates each algorithm against key performance metrics, including energy efficiency, task latency, convergence behavior, load balancing, and system fault tolerance. The results unequivocally demonstrate that PSO outperforms both ACO and ABC across most performance dimensions, exhibiting faster convergence, superior energy utilization, more effective latency management, and enhanced fault recovery capabilities. While ABC demonstrates competitive performance in flexibility and fairness, ACO shows significant limitations in time-sensitive and failure-prone scenarios. This research contributes a modular simulation framework suitable for real-time edge computing applications and offers practical guidance for deploying adaptive optimization strategies in urban energy systems. Ultimately, our findings underscore the critical importance of algorithm selection in smart city energy infrastructure and highlight the potential of swarm-based intelligence to enable scalable, resilient, and efficient resource management in the sustainable cities of the future.
Big Data and Data Mining for Efficient Energy Storage and Management Nazar, Mustafa; Ali, Zaid Ghanim; Adnan, Kahtan Mohammed; Khalil, Ibraheem Mohammed; Nassar, Waleed; Maidin, Siti Sarah
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1759

Abstract

The rapid expansion of decentralized and renewable energy systems necessitates intelligent strategies for energy storage and management. This paper presents a comprehensive framework that leverages big data analytics and data mining to optimize energy storage systems within smart grid architectures. By integrating high-frequency data from IoT-enabled Li-Ion batteries, flow batteries, supercapacitor arrays, and hybrid systems, our methodology enhances storage efficiency, predictive accuracy, and fault detection. The approach uniquely combines an ensemble forecasting model (Random Forest and XGBoost), which achieved a 97% R² score in predicting energy demand, with Gaussian Mixture Models for consumer pattern clustering and canonical correlation analysis to model the impact of environmental variables. Validation on real-world datasets demonstrates significant performance gains without additional hardware. For instance, algorithmic optimization improved the round-trip efficiency of a Hybrid Battery Energy Storage System from 86.7% to 93.3% and a Li-Ion battery by 7%. The study underscores the critical influence of contextual variables like temperature and humidity on state-of-charge stability. Furthermore, the analytical framework demonstrated a 50% increase in system throughput (from 34 to 51 tasks/sec) after optimization. This research provides a replicable, data-driven model for deploying intelligent analytics in both microgrid and industrial-scale settings, paving the way for more adaptive and resilient energy infrastructures. Future work will explore edge computing and reinforcement learning to further enhance scalability and autonomy.
Co-Authors Abdul Radhi, Rafah Hassan Adnan, Kahtan Mohammed Ali, Zaid Ghanim Jafar, Qusay Mohammed Khalil, Baker Mohammed Khalil, Ibraheem Mohammed Maidin, Siti Sarah Majeed, Adil Abbas Nassar, Waleed