Twin4Build in Action: AI-Powered Digital Twins Advancing Building Decarbonization with Key Findings and Lessons Learned

Given the urgency of addressing climate change, the decarbonization of the built environment is crucial for meeting global climate targets and ensuring the long-term resilience of urban infrastructure. Traditional energy management strategies in buildings, often based on outdated and static models, are no longer adequate to address real-time variables, leading to inefficiencies and increased energy consumption. In response to these challenges, Digital Twin (DT) technology offers a promising solution. A Digital Twin is a dynamic, real-time model that continuously integrates data from building systems, sensors, and user interactions, enabling actionable insights to optimize energy use and reduce emissions. When enhanced with Artificial Intelligence (AI), DTs can predict energy demand, optimize building operations, and support sustainable decision-making throughout the entire building lifecycle, from design to decommissioning. This paper presents a novel Digital Twin platform developed as part of the Twin4Build project, which combines adaptable energy models with real-time data to enhance energy efficiency, operational performance, and collaborative decision-making. Case studies from the Twin4Build project illustrate the platform's scalability and effectiveness, showcasing its potential to transform building management and contribute significantly to the decarbonization of the building sector.