Artificial Intelligence-Based Optimization Framework for Smart Campus Environments: Enhancing Efficiency, Comfort, and Safety
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Keywords:
Smart Campus, Energy Optimization, Artificial Intelligence, Comfort and Safety, Machine LearningAbstract
The growing energy demand and operational complexity of modern educational institutions
highlight the need for intelligent and sustainable energy management. This thesis introduces an artificial
intelligent (AI)-based energy optimization framework tailored for smart campuses (SCs), aiming to reduce
energy consumption, enhance user comfort, and improve safety. The proposed system integrates real-time
data from IoT sensors—monitoring variables such as temperature, humidity, occupancy, lighting, CO₂, and
motion—with machine learning algorithms including Artificial Neural Networks (ANN), Convolutional
Neural Networks (CNN), and Reinforcement Learning (RL).
The architecture consists of three core layers: sensing, communication, and computation. Edge devices
(e.g., Raspberry Pi, Jetson Nano) perform local data preprocessing and communicate with a centralized AI
server using MQTT protocols. The AI engine analyzes incoming data, prioritizes safety events, adjusts
environmental conditions to ensure comfort, and applies optimization techniques to minimize energy use.
Based on architectural design and literature-aligned estimations, the system demonstrates a potential energy
saving of approximately 59.125%, translating to substantial financial benefits and a shorter payback period
for large campuses. Additional features include vision-based safety monitoring, anomaly detection, and
adaptive learning capabilities.
Although implementation is currently at the design stage, this framework offers a scalable and realistic
solution for smart campus (SC) transformation. Future work will focus on real-world deployment, system
validation, and integration of cybersecurity and user-centric features.
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