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    A Model for Artificial Intelligence Supported Energy Management in Smart Campuses
    (Sakarya University of Applied Sciences, 2025) Manassra, Abdalhadi; Işık, Gürkan
    Rising energy consumption and inefficiencies in large-scale facilities, such as university campuses, present critical financial and environmental challenges. Traditional energy management systems rely on static strategies, failing to adapt to real-time variations in demand, which leads to unnecessary energy waste and increased operational costs. This study introduces an AI-driven integrated energy management framework that utilizes real-time data from IoT sensors to optimize energy consumption across key campus systems, such as lighting, ventilation, heating, air conditioning, renewable energy sources, information and communication technology infrastructure, and building energy management systems. By leveraging machine learning techniques such as Artificial Neural Networks, Convolutional Neural Networks, and Reinforcement Learning, the system has potential to adjust energy-intensive operations, achieving a 59.125% reduction in total energy consumption. This translates into substantial financial savings of ?7,390,625 annually for a mid-sized campus and a significantly lower carbon footprint, with heating cooling and lighting optimizations delivering the most significant impact. A cloud-edge computing architecture is integrated to enable real-time decision-making, ensuring efficient energy distribution without compromising user comfort or operational efficiency. However, the system's effectiveness depends on high-quality sensor data, adaptive AI algorithms, and robust cybersecurity measures to protect the IoT-based infrastructure. The results highlight the transformative potential of Artificial Intelligence in sustainable energy management, demonstrating that smart campus implementations can significantly reduce costs, enhance efficiency, and set a benchmark for autonomous AI-driven energy optimization in facilities.