Start Time:
10/16/2025 8:00:00 AM
End Time:
10/16/2025 8:50:00 AM
About this session:
In this presentation, we explore the transformative potential of AI-driven digital twins in facility management. By leveraging advanced AI techniques and digital twin technology, we can monitor and analyze building system utilization, uncovering patterns in occupant behavior. These insights enable us to optimize building operations, resulting in significant efficiency improvements and cost savings. Attendees will gain an understanding of the foundational concepts of AI and digital twins, and how they can be applied to track and enhance building performance. We will delve into the practical steps for implementing digital twins, including data collection through IoT devices and the use of machine learning algorithms for pattern recognition. Real-world case studies will illustrate the substantial efficiency gains, with potential savings of up to 27%. Additionally, we will address common challenges and provide solutions to ensure successful integration of AI-driven digital twins. Join us to discover how this cutting-edge technology can revolutionize facility management, driving smarter, more efficient buildings for a sustainable future.
1. Understand the Fundamentals of AI and Digital Twins: Attendees will gain a foundational understanding of AI and digital twin technology, including their definitions, key components, and applications in facility management.
2. Identify Patterns in Building System Utilization: Participants will learn how to use AI to monitor and analyze building system utilization, recognizing patterns in occupant behavior that can inform operational adjustments.
3. Implement AI-Driven Digital Twins for Efficiency: Attendees will be equipped with practical knowledge on how to create and integrate digital twins into building systems, including data collection methods and machine learning techniques.
4. Achieve and Measure Efficiency Savings: Participants will explore strategies for optimizing building operations based on AI insights, with a focus on achieving and verifying efficiency savings of up to 27%.