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International Journal of Engineering, Science and Information Technology
Published by Universitas Malikussaleh
ISSN : -     EISSN : 27752674     DOI : -
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The journal covers all aspects of applied engineering, applied Science and information technology, that is: Engineering: Energy Mechanical Engineering Computing and Artificial Intelligence Applied Biosciences and Bioengineering Environmental and Sustainable Science and Technology Quantum Science and Technology Applied Physics Earth Sciences and Geography Civil Engineering Electrical, Electronics and Communications Engineering Robotics and Automation Marine Engineering Aerospace Science and Engineering Architecture Chemical & Process Structural, Geological & Mining Engineering Industrial Mechanical & Materials Science: Bioscience & Biotechnology Chemistry Food Technology Applied Biosciences and Bioengineering Environmental Health Science Mathematics Statistics Applied Physics Biology Pharmaceutical Science Information Technology: Artificial Intelligence Computer Science Computer Network Data Mining Web Language Programming E-Learning & Multimedia Information System Internet & Mobile Computing Database Data Warehouse Big Data Machine Learning Operating System Algorithm Computer Architecture Computer Security Embedded system Coud Computing Internet of Thing Robotics Computer Hardware Information System Geographical Information System Virtual Reality, Augmented Reality Multimedia Computer Vision Computer Graphics Pattern & Speech Recognition Image processing ICT interaction with society, ICT application in social science, ICT as a social research tool, ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 82 Documents
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Search results for , issue "Vol 5, No 1 (2025)" : 82 Documents clear
Computer Vision for Monitoring Renewable Energy Infrastructure Hussein, Ahmed Ali; Alal, Sumaia Ali; Abdulrahman, Saad Abdulaziz; Merzah, Hanaa Hameed; Abbas, Hasan Ali; Batumalay, M.
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.v5i1.1727

Abstract

The operational efficiency of renewable energy installations, including solar, wind, and hydropower systems, is often hindered by the limitations of manual inspections and legacy monitoring. These methods lack the real-time, scalable fault detection necessary to prevent costly downtime. This paper proposes a comprehensive computer vision framework for automated fault detection, predictive maintenance, and inspection optimization across diverse renewable energy infrastructures. We developed a hybrid deep learning model, based on ResNet-50 with attention-based extensions, to analyze high-resolution imagery from drones and stationary cameras. The model was trained and validated on a dataset of 20,000 labeled images covering infrastructure-specific defects such as photovoltaic microcracks, wind turbine blade erosion, and hydropower sedimentation patterns. Our experiments demonstrate high-performance, with fault detection accuracy exceeding 91% for all categories and inference latencies under 70ms. The system significantly improved predictive maintenance outcomes, reducing unplanned outages by over 77% and decreasing inspection energy consumption by more than 70%. Scalability tests on a larger 50,000-image dataset confirmed the framework's robustness, maintaining high accuracy and processing speed. This work validates computer vision as a viable, cost-effective, and scalable solution for intelligent monitoring in the renewable energy sector, offering significant practical implications for autonomous diagnostic systems in smart grid and industrial applications for energy efficiency.
Context-Aware Systems for Proactive Energy Efficiency Services Hameed, Maan; Noori, Nabaa Ahmed; Suleiman, Aghaid Khudr; Abu-AlShaeer, Mahmood Jawad; Sabah, Ahmed; Batumalay, M.
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.v5i1.1728

Abstract

Static energy control systems are increasingly unable to meet the demands of modern built environments, where dynamic occupancy and fluctuating conditions lead to significant inefficiencies. This paper presents a context-aware system for proactive energy management that integrates real-time data acquisition, machine learning-based forecasting, and autonomous control. A multi-tiered architecture was developed and deployed across diverse settings residential, commercial, and industrial—to gather contextual data on temperature, occupancy, lighting, and equipment usage. The system uses predictive forecasting to anticipate short-term energy needs and reinforcement learning to optimize control strategies, ensuring both energy savings and user comfort. Results from the deployment demonstrate significant power reduction, high system responsiveness, and strong user satisfaction. Application-specific benchmarks revealed major efficiency gains in HVAC, lighting, and industrial machinery, while scalability tests confirmed stable performance under increasing sensor loads. This research validates the effectiveness of combining contextual intelligence with adaptive control to create sustainable, responsive, and human-centered energy systems. We provide a practical, modular framework for intelligent energy infrastructure in smart buildings and industrial parks. Future work will focus on enhancing model interpretability, integrating economic incentives, and exploring federated learning for distributed intelligence in support of energy efficiency.

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