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Sabah, Ahmed
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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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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.
Data-Driven Cloud Systems for Renewable Energy Optimization Yousif, Hayder Abdulameer; Hussain, Salah Yehia; Hassan Ali, Taif Sami; Al-Doori, Vian S.; Sabah, Ahmed; Batumalay, M.
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1729

Abstract

The growing share of renewable generation in global power systems creates operational instability due to the volatile nature of solar, wind, and hydropower. This study presents a novel cloud-edge integrated model designed to enhance the performance and efficiency of these renewable sources through a data-centric approach. The proposed architecture relies on an IoT-enabled sensor network for real-time data gathering, processed through a hybrid infrastructure combining edge-level filtration with cloud-based analytics. For energy output prediction, we compared Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) models, with LSTM demonstrating superior performance. To optimize operations, a multi-objective Genetic Algorithm was implemented to simultaneously minimize energy losses and costs while improving grid utilization balance. Furthermore, exergy-based modeling was employed to evaluate the thermodynamic quality of energy transformations. The results confirmed that the system significantly improved predictive accuracy, responsiveness, and energy savings. Under varying loads, the system maintained low latency and high energy allocation efficiency, validating its real-time adaptability. In summary, this research delivers a modular and scalable solution for intelligent energy management, highlighting the power of predictive analytics and adaptive control in creating data-driven, next-generation sustainable energy efficiency systems.
Co-Authors Abu-AlShaeer, Mahmood Jawad Al-Doori, Vian S. Batumalay, M. Hameed, Maan Hassan Ali, Taif Sami Hussain, Salah Yehia Noori, Nabaa Ahmed Suleiman, Aghaid Khudr Yousif, Hayder Abdulameer