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Digital Twin Smart City Visualization with MoE-Based Personal Thermal Comfort Analysis
Hoang-Khanh Lam1, Phuoc-Dat Lam1, Soo-Yol Ok1
1Department of Computer Engineering, Dong-A University, Busan 49315, Republic of Korea.
Sensors (Basel, Switzerland)
|February 13, 2025
Summary
Digital twin technology and AI analysis create virtual city models for sustainable urban planning. This study visualizes a smart area in South Korea, optimizing energy use through personal thermal comfort analysis.
Area of Science:
- Urban planning and sustainable development
- Artificial Intelligence (AI) and digital twin technology
- Geospatial data visualization
Background:
- Increasing urbanization necessitates sustainable development strategies.
- Digital twin technology offers real-time simulation for complex systems.
- AI-driven analysis enhances the capabilities of digital twins for urban management.
Purpose of the Study:
- To develop a digital twin application for visualizing a smart urban area.
- To implement deep learning for personal thermal comfort analysis.
- To provide a roadmap for smart city application development.
Main Methods:
- Utilizing Cesium for Unreal for 3D geospatial data integration.
- Employing DataSmith to convert 3D data into Unreal Engine format.
- Integrating a deep learning model for thermal comfort analysis within the digital twin framework.
Main Results:
- Successful visualization of a smart area in South Korea using digital twin technology.
- Demonstration of personal thermal comfort analysis for energy saving potential.
- Establishment of a framework for smart city application development.
Conclusions:
- Digital twin technology combined with AI is crucial for future urban environments.
- The developed application aids in managing and saving building and household energy consumption.
- This research lays the groundwork for future innovations in urban planning and management.
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