Research: Integrating Digital Twin and BIM Models for Predictive Energy and Maintenance...

Research question: How can digital twin and BIM-based models accurately predict building energy consumption and maintenance requirements throughout a building’s lifecycle?

Background & Motivation: With increasing emphasis on sustainability and operational efficiency in the built environment, digital twin and Building Information Modeling (BIM) technologies are being deployed to monitor and optimize building performance. These technologies enable real-time data integration and visualization, offering opportunities for predictive analytics in energy use and maintenance.

Research Gap & Question: Despite advancements, there is limited empirical evidence on the accuracy and reliability of digital twin and BIM-driven predictions over long-term building lifecycles. Most studies focus on short-term simulations or isolated parameters, rather than holistic, lifecycle-based forecasting.

Approach & Expected Contribution: This research will synthesize existing literature, develop a methodology for integrating digital twin and BIM data, and evaluate predictive models using real-world datasets. The study will compare predicted versus actual energy consumption and maintenance events, quantifying predictive accuracy and identifying key influencing factors.

Why It Matters: Improving predictive capabilities is crucial for resilient, cost-effective, and low-carbon infrastructure. The results could inform industry adoption of digital twin frameworks for proactive facilities management, supporting sustainable building operations and policy development.