Digital Twins for Real Estate & Buildings

A digital model of a building or facility which replicates the facility’s physical properties, systems and processes, and simulates its performance using real-time and historical data from many sources – such as plans, sensors, building equipment, staff badges – and a comprehensive analysis toolkit – such as graph databases, physics-based models, reduced form FEA models, machine learning models – to enhance operational decision-making and automation for that facility. This category focuses on digital twins for non-industrial, commercial real estate asset types, such as offices, educational campuses, healthcare facilities, hospitality and event spaces.

In pre-operation phases, digital twins can be used to simulate the energy performance of various building designs via physics-based modelling capabilities and also accelerate operational readiness at handover Integrate disparate building systems and data, leveraging data semantic models and knowledge graph databases, to provide the data new context, unlock new use cases and allow users to compare real-time performance against predicted performance as well as to ask new questions based on these newly combined sets. Optimize building operations and workforce productivity with enhanced predictive analytics, FDD, automated building system operations, maintenance task prioritization, what-if analyses and building simulations.