Akıllı Kampüsler için Yapay Zekâ Tabanlı Enerji Optimizasyonunun Tekno-Ekonomik Doğrulaması: Bir Dijital İkiz Simülasyonu Yaklaşımı

Öz

Orta ve büyük ölçekli eğitim tesisleri ve benzer altyapıya sahip komplekslerde artan enerji tüketimi, dinamik işletme koşullarına uyum sağlayabilen akıllı yönetim sistemlerini zorunlu kılmaktadır. Bu çalışma, akıllı kampüsler için önerilen melez yapay zekâ tabanlı bir enerji optimizasyon modelinin teknik performansını ve ekonomik fizibilitesini değerlendirmektedir. Yüksek kullanıcı yoğunluğuna ve 100.000 metrekarelik kapalı alana sahip orta ölçekli bir üniversite kampüsünün (idari, laboratuvar, derslik ve kafeterya binaları dâhil) dijital ikizi, farklı yaz ve kış senaryoları altında gerçek zamanlı operasyonları simüle etmek için geliştirilmiştir. Kontrol mimarisi, ısıtma, havalandırma ve iklimlendirme optimizasyonu için Derin Q-Ağları, hareket tabanlı aydınlatma kontrolü için Evrişimsel Sinir Ağları ve fiş yükü yönetimi için Çok Katmanlı Algılayıcıları entegre etmektedir. Teorik modellerin aksine, bu çalışma performansı titiz bir simülasyon yoluyla doğrulamaktadır. Sonuçlar önerilen sistemin geleneksel kural tabanlı temellere kıyasla ortalama %44,5 enerji tasarrufu sağladığını ve %91'lik bir kullanıcı konfor puanını koruduğunu göstermektedir. Çalışma ayrıca, bu simülasyonla doğrulanmış tasarrufları kullanarak ekonomik fizibiliteyi yeniden hesaplamaktadır. Finansal analiz, yüksek başlangıç sermaye harcamalarına rağmen sistemin 6 yıl içinde başabaş noktasına ulaştığını ve %20'lik bir İç Getiri Oranında istikrar kazandığını ortaya koymaktadır. Bu bulgular, otonom otomasyonun yalnızca teknik olarak sağlam olmakla kalmayıp, aynı zamanda sürdürülebilir kampüs altyapısı için finansal olarak da uygulanabilir bir çözüm sunduğunu doğrulamaktadır.

Anahtar Kelimeler

• Akıllı kampüs
• Melez yapay zekâ
• Dijital ikiz
• Ekonomik fizibilite
• Enerji optimizasyonu.

Techno-Economic Validation of AI-Based Energy Optimization for Smart Campuses: A Digital Twin Simulation Approach

Abstract

Rising energy consumption in mid-to-large scale educational facilities and complexes with similar infrastructure necessitates intelligent management systems capable of adapting to dynamic operational conditions. This study evaluates the technical performance and economic feasibility of a proposed hybrid Artificial Intelligence-based energy optimization model for smart campuses. A digital twin of a mid-sized university campus with high user density and a 100,000 square meter indoor area (comprising administrative, laboratory, classroom, and cafeteria buildings) was developed to simulate real-time operations under distinct summer and winter scenarios. The control architecture integrates Deep Q-Networks for heating, ventilation, and air conditioning optimization, Convolutional Neural Networks for motion-based lighting control, and Multi-Layer Perceptrons for plug load management. Unlike theoretical models, this study validates performance through rigorous simulation. The results demonstrate that the proposed system achieves an average energy saving of 44.5% compared to traditional rule-based baselines, while maintaining a user comfort score of 91%. The study further recalculates economic feasibility using these simulation-verified savings. Financial analysis reveals that despite high initial capital expenditures, the system achieves a break-even point within 6 years and stabilizes at an Internal Rate of Return of 20%. These findings confirm that autonomous automation is not only technically robust but also presents a financially viable solution for sustainable campus infrastructure.

Keywords

• Smart campus
• Hybrid artificial intelligence
• Digital twin
• Economic feasibility
• Energy optimization.

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