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All Journal JOIV : International Journal on Informatics Visualization International Journal of Engineering, Science and Information Technology
Hameed, Maan
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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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Drones and IoT for Enhancing Renewable Energy Integration Hameed, Maan; Abdulkareem Hameed, Nada; Natiq Abdulwahab, Imad; Hashim Qasim, Nameer; S. Alani, Saad
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4630

Abstract

Ranging from monitoring in real-time to predictive maintenance and operational optimization, the increasing complexity of renewable energy systems requires sophisticated solutions. The article proposes a holistic solution that combines drones and IoT to improve installation efficiency and reduce incidents in wind, solar, and hydropower energy production. The study uses a hybrid approach that combines sensor analytics, drone-assisted infrastructure inspection, edge computing for latency minimization, and multivariate modeling to quantify the system's enhancement. Field trials involved three renewable power plants over the course of six months and included the acquisition of more than 10,000 data related to power plant operations. It was shown that integrating a thermal, an RGB, and a LiDAR sensor on a drone resulted in a significant increase in inspection efficiency, fault coverage, and spatial resolution. At the same time, deployed IoT sensors continuously monitor inverter temperature, vibration frequency, and energy output. Statistical regression models revealed highly significant relationships among the frequency of UAV inspection, IoT latency, and energy efficiency, and algorithmic modules, such as support vector machines, Kalman filters, and ant colony optimization, further improved fault diagnosis, data fusion, and pathfinding. The results validate the applicability of drones and IoT for enhancing system uptime, dependability, and predictability without introducing extra operational load. This work lays out a scalable, modular approach, feasible for deployment in smart grid scenarios, which enables sustainable, intelligent energy management.
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.
Co-Authors Abdulkareem Hameed, Nada Abu-AlShaeer, Mahmood Jawad Batumalay, M. Hashim Qasim, Nameer Natiq Abdulwahab, Imad Noori, Nabaa Ahmed S. Alani, Saad Sabah, Ahmed Suleiman, Aghaid Khudr