Self-Learning Digital Twins for Smart Heating

This presentation introduces an innovative AI-driven, self-learning smart-heating digital twin solution designed to reduce the carbon footprint of existing buildings. Unlike traditional efficiency strategies, this edge-native approach leverages reality gamification and collaborative strategies to optimize building performance. Recently published in the DTC Technology Showcase, the solution integrates BIM models, wireless sensors, and actuators with game engine technology to create an immersive, real-time collaboration arena for stakeholders. WHAT WILL BE COVERED 1. Edge-native digital twin strategies for existing buildings 2. AI-driven predictions and automated actuation for carbon footprint reduction 3. Reality gamification for enhanced stakeholder collaboration 4. Integration of real-time data with business applications and decision-making processes Nicolas will discuss practical applications of this technology in the real estate sector, demonstrating how it enhances energy efficiency, reduces maintenance costs, and improves overall building performance. The presentation will highlight how this approach achieves up to 25% energy savings and significant cost and time savings. The presentation will conclude with a demonstration of the solution's ability to transform building operations through intelligent automation and continuous optimization. WHO SHOULD ATTEND • Real estate owners, managers, and developers interested in innovative solutions for reducing the carbon footprint of existing buildings. • Technologists and business executives across industries committed to digital twins and seeking to improve operational efficiency in building management. • Sustainability professionals looking for advanced technologies to meet climate goals and promote sustainable development practices. • Anyone interested in the practical applications of digital engineering, digital twins, 3D modeling, and simulation in the built environment.