A Model for Artificial Intelligence Supported Energy Management in Smart Campuses

Year 2025, Volume: 6 Issue: 2, 74 - 92, 18.12.2025

Abdalhadi Manassra , Gürkan Işık

https://doi.org/10.58769/joinssr.1677699

Abstract

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.

Keywords

Artificial intelligence , Deep learning , Energy management , HVAC , Smart campus.

Akıllı Kampüslerde Yapay Zeka Destekli Enerji Yönetimi için Bir Model

Abstract

Üniversite kampüsleri gibi büyük ölçekli tesislerde artan enerji tüketimi ve verimsizlikler, ciddi finansal ve çevresel sorunlar doğurmaktadır. Geleneksel enerji yönetim sistemleri, talepteki anlık değişimlere uyum sağlayamayan statik stratejilere dayanmakta olup, bu durum gereksiz enerji israfına ve artan işletme maliyetlerine yol açmaktadır. Bu çalışma, kampüs genelinde aydınlatma, havalandırma, ısıtma, iklimlendirme, yenilenebilir enerji kaynakları, bilgi ve iletişim teknolojileri altyapısı ile bina enerji yönetim sistemleri gibi temel sistemlerde enerji tüketimini optimize etmek amacıyla IoT sensörlerinden alınan gerçek zamanlı verileri kullanan, yapay zeka destekli entegre bir enerji yönetim çerçevesi sunmaktadır. Yapay Sinir Ağları, Evrişimli Sinir Ağları ve Pekiştirmeli Öğrenme gibi makine öğrenmesi tekniklerinden yararlanan sistem, enerji yoğun işlemleri optimize ederek toplam enerji tüketiminde %59,125 oranında azalma sağlama potansiyeline sahiptir. Bu da orta ölçekli bir kampüs için yıllık ₺7.390.625 tutarında önemli bir finansal tasarrufa ve daha düşük bir karbon ayak izine karşılık gelmektedir; özellikle ısıtma, soğutma ve aydınlatma optimizasyonları en büyük etkiyi yaratmaktadır. Gerçek zamanlı karar alma süreçlerini mümkün kılmak için bulut-kenar (cloud-edge) bilişim mimarisi entegre edilmiştir; bu sayede kullanıcı konforu veya operasyonel verimlilikten ödün vermeksizin etkin enerji dağıtımı sağlanmaktadır. Ancak sistemin etkinliği, yüksek kaliteli sensör verilerine, uyum sağlayabilen yapay zeka algoritmalarına ve IoT tabanlı altyapıyı koruyacak sağlam siber güvenlik önlemlerine bağlıdır. Elde edilen sonuçlar, yapay zekanın sürdürülebilir enerji yönetimindeki dönüştürücü potansiyelini ortaya koymakta; akıllı kampüs uygulamalarının maliyetleri önemli ölçüde azaltabileceğini, verimliliği artırabileceğini ve tesislerde otonom yapay zeka destekli enerji optimizasyonu için bir referans oluşturabileceğini göstermektedir.

Keywords

Yapay zeka , Derin öğrenme , Enerji yönetimi , Akıllı kampüs , HVAC

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