Digital Twin-Enabled High Flexibility Building Systems: A Comprehensive Framework for Energy Optimization

Abstract

This paper presents a comprehensive framework that harnesses the power of digital twin technology to enhance the energy optimization of high flexibility building systems. The study focuses on buildings with adaptable layouts, multi-use spaces, and variable occupancy patterns. By leveraging the accurate representation and real-time data provided by digital twin models, the framework enables advanced predictive control algorithms to optimize energy consumption based on the dynamic nature of the building. The proposed approach integrates occupancy predictions, weather forecasts, and energy market data to make informed decisions for energy demand response, load shifting, and equipment scheduling. Through simulation studies, the effectiveness of the framework in achieving significant energy savings, reducing peak demand, and improving overall system performance is demonstrated. The findings highlight the potential of digital twin technology in enabling high flexibility building systems that adapt to changing conditions and optimize energy usage in a sustainable and efficient manner.