Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Engineering, Science and Information Technology
Thamer Kadum Yousif Al Hilfi
Madenat Alelem University College
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

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Design and Deployment of a Secure Cyber-Physical System for Energy Monitoring in Smart Agriculture Dina Fallah; Elaf Sabah Abbas; Mohsen Ali Ahmed; Wafaa Adnan Sajid; Thamer Kadum Yousif Al Hilfi; Siti Sarah Maidin
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.1398

Abstract

The growing need for sustainable agricultural practices has spurred the integration of cyber-physical systems (CPS) into modern farming. This paper presents the design, deployment, and evaluation of a modular CPS architecture for adaptive energy monitoring and control in smart agriculture. The system integrates environmental sensing, predictive modelling, and optimisation-guided actuation to enhance energy efficiency and operational resilience. Field tests on a 3-hectare site across six crop environments demonstrated significant performance gains, achieving energy savings of up to 25.8% and peak demand reductions of up to 19.8%. Our multi-layer architecture, featuring STM32 microcontrollers, LoRaWAN communication, and a cloud analytics dashboard, enables proactive control by anticipating energy demand using an LSTM-NARX predictive model. This approach reduced control actuation delay to 1.8 seconds and proved robust against cyber-physical faults, recovering from communication failures and data anomalies in under 15 seconds. The results validate that embedding energy-aware, predictive logic into CPS infrastructure creates scalable, efficient, and reliable agricultural solutions. We acknowledge limitations related to predictive model complexity and communication latency, and we propose future work focused on distributed CPS coordination, federated learning, and full lifecycle sustainability analysis to further advance intelligent, resource-efficient agriculture.
Algorithms and Modeling for Optimizing Sustainable Energy Systems Mohammed Abdul Jaleel Maktoof; Alhamza Abdulsatar Shaker; Hamdi Abdullah Nayef; Nada Adnan Taher; Thamer Kadum Yousif Al Hilfi; Siti Sarah Maidin
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 Alhamza Abdulsatar Shaker Dina Fallah Elaf Sabah Abbas Hamdi Abdullah Nayef Mohammed Abdul Jaleel Maktoof Mohsen Ali Ahmed Nada Adnan Taher Siti Sarah Maidin Siti Sarah Maidin Wafaa Adnan Sajid