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All Journal International Journal of Engineering, Science and Information Technology
Jaleel Maktoof, Mohammed Abdul
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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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Algorithms and Modeling for Optimizing Sustainable Energy Systems Jaleel Maktoof, Mohammed Abdul; Shaker, Alhamza Abdulsatar; Nayef, Hamdi Abdullah; Taher, Nada Adnan; Yousif Al Hilfi, Thamer Kadum; 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.v5i1.1457

Abstract

The global transition toward sustainable energy necessitates intelligent, integrated solutions to overcome the intermittency of renewable sources. This paper presents and validates a comprehensive framework for optimising Hybrid Solar-Wind Energy (HSWE) systems by integrating advanced simulation, machine learning-based forecasting, and metaheuristic optimisation. Using meteorological and operational data from three distinct climate zones, we modelled and analysed a PV-wind-lithium-ion hybrid system. A neural network was employed for precise load forecasting, while Particle Swarm Optimisation (PSO) managed real-time resource allocation and storage dispatch. Comparative analysis reveals that the optimised hybrid system significantly outperforms standalone units, increasing energy production by up to 32%, improving overall energy efficiency to 92.3%, and reducing operational costs by over 36%. The simulation models demonstrated high fidelity, with predictions matching experimental field data with less than 1% error. Furthermore, the integration of predictive fault handling and intelligent load balancing enhanced system reliability, increasing the mean time between failures (MTBF) by over 70% and achieving 97.6% system availability. This research provides a validated, replicable framework for engineers and policymakers, demonstrating a practical pathway to developing efficient, economically viable, and resilient decentralised renewable energy infrastructure to meet global sustainability goals.
Co-Authors Maidin, Siti Sarah Nayef, Hamdi Abdullah Shaker, Alhamza Abdulsatar Taher, Nada Adnan Yousif Al Hilfi, Thamer Kadum