Ibrahim Abbas Elamen Ahmed

• Islam, Fayzul; Ahmed, Ibrahim Abbas Elamen & Mihet, Nicolae Lucian (2025). Demand-Side Management in Residential Buildings: Load Shifting and Cost Savings via a Cloud-Based IoT-BEMS. In 2025, IECON (Eds.), IECON 2025 – 51st Annual Conference of the IEEE Industrial Electronics Society. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331596811. p. 1–7. doi: 10.1109/IECON58223.2025.11221160. Full text in Research Archive
• Islam, Fayzul; Ahmed, Ibrahim Abbas Elamen & Mihet-Popa, Lucian (2025). Development and testing of an IoT platform with smart algorithms for building energy management systems. Energy and Buildings. ISSN 0378-7788. 344. doi: 10.1016/j.enbuild.2025.115970. Full text in Research Archive Show summary

  Buildings are a major cause of carbon emissions. The building sector is responsible for around 40% of energy consumption and for about 30% of CO2 emissions. Building Energy Management Systems (BEMS) are crucial for enhancing energy efficiency, and energy flexibility, and mitigating the environmental impact of buildings, which account for a significant amount of global energy use and greenhouse gas emissions. This study addresses the limitations of traditional BEMS by proposing a cloud-based IoT-BEMS with an intuitive user interface and advanced machine learning algorithms for energy optimization. The system integrates demand-side management techniques, including two main principles, load shifting through demand response and energy efficiency, allowing users to control appliances without requiring technical expertise. The results demonstrate significant energy savings, particularly in water heater optimization, with an average reduction of 24.23% in energy consumption. Additionally, the Proximal Policy Optimization (PPO) algorithm used for electric vehicle (EV) charging resulted in an average cost saving of 30.6% by leveraging off-peak electricity rates. The platform’s real-time data processing and user-friendly interface make it a robust solution for residential energy management, effectively balancing energy savings with user comfort. This research underscores the potential of IoT and machine learning in revolutionizing building energy management, contributing to the Sustainable Development Goals.

• Upadhyay, Saugat; Ahmed, Ibrahim Abbas Elamen & Mihet-Popa, Lucian (2024). Energy Management System for an Industrial Microgrid Using Optimization Algorithms-Based Reinforcement Learning Technique. Energies. 17(16). doi: 10.3390/en17163898. Full text in Research Archive Show summary

  The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront and the research interest in microgrids which rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper proposes an optimization algorithm based on reinforcement learning in an industrial microgrid that is capable of trading energy with the main grid, providing significant cost savings. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared to logic-based optimization and 86% savings compared to not using any optimization. These findings contribute to digitalization and decarbonization of energy technology and support fundamental goals and policies of the European Green Deal.

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• Mihet-Popa, Lucian; Ahmed, Ibrahim Abbas Elamen & Islam, Fayzul (2025). NIKT 2025 – Institutt for informasjonsteknologi og kommunikasjon – Høgskolen i Østfold. Full text in Research Archive Show summary

  Our workshop focuses on how emerging digital tools and intelligent algorithms, grounded in artificial intelligence (AI) and machine learning (ML), can be harnessed to enhance research processes across disciplines. With the rapid growth of data and computational power, AI- and ML-driven solutions are becoming indispensable for data analysis, pattern recognition, prediction, optimization, and decision support in research. The importance of these workshops lies in their role as collaborative arenas where researchers, practitioners, and students can share insights, test new ideas, and build networks. By tackling both theoretical advances and practical applications, our workshops contribute directly to the overarching goals of NIKT: fostering interdisciplinary collaboration, strengthening Norway’s ICT research landscape, and preparing society for rapid digital transformation. The relevance of this topic to NIKT is twofold. First, it strengthens the ICT research community by providing a platform for discussing state-of-the-art methods, tools, and frameworks that can improve the quality, efficiency, and reproducibility of research. Second, it bridges the gap between theory and practice by highlighting concrete applications—ranging from healthcare, climate science, and engineering to the humanities—showing how AI and ML can accelerate discovery and innovation. By bringing together researchers, practitioners, and students, the workshop fosters interdisciplinary collaboration, encourages knowledge transfer, and equips participants with insights into deploying smart algorithms and digital tools responsibly and effectively. Ultimately, this aligns with NIKT’s mission of advancing ICT research and ensuring that Norway remains at the forefront of digital transformation and innovation. Norsk IKT-konferanse for forskning og utdanning 24 - 26. November 2025 i Halden

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