A digital twin architecture for real-time and offline high granularity analysis in smart buildings

Abstract

• Building digital twin architecture for real-time or faster-than-real-time execution. • Integration of intelligent monitoring and control schemes for smart buildings . • Modelling of indoor environment quality, electricity consumption, and user behavior. • Investigation of what if scenarios through Hardware-In-The-Loop configurations. • Computational fluid dynamics modeling for spatiotemporal high granularity analysis. Smart buildings aim to create a safe, comfortable and sustainable environment for the occupants while increasing the energy performance of the building to reduce the environmental footprint and operational cost. Towards this direction, the use of model-based digital twins able to simulate key aspects of the building is important. Despite the utilization of various digital twin approaches for smart buildings in recent years, most of these approaches mainly focus on one aspect of the building, such as indoor environmental quality or energy consumption. This observation necessitates the development of a virtual but realistic and non-invasive environment able to simulate all the key domains of the building, including indoor air quality, thermal comfort, and energy consumption. Towards this direction, this work proposes an architecture for smart buildings investigations, that emulates the thermal comfort, air-quality, and electricity consumption of the building, while considering the activities and behavior of occupants. For simulating the building operations, ordinary differential equations and power balance modeling are incorporated to facilitate real-time or faster than real-time digital twin approaches, while computational fluid dynamics modeling is used complementarily, to enable higher granularity simulations for offline analysis. Both models have been validated in a real building achieving less than 1% average zone temperature error between the simulation models and the actual building sensor measurements. Furthermore, a user-friendly software platform has been developed and integrated with the proposed architecture, where multiple important features and characteristics have been included to enable various investigations. In particular, the developed platform enables physical and simulated building integration, model calibration using field measurements, experimental configuration and management, execution of novel what-if investigations, and evaluation of novel algorithms using hardware-in-the-loop configurations. The proposed architecture is applied in a physical building, allowing the demonstration of several investigations and the execution of interesting use-cases. The proposed smart buildings digital twin consists of four main layers: (a) the building data layer that includes information regarding the geometry and the materials; (b) the live measurements layer that includes the indoor air quality sensors, electricity meters and live weather measurements; (c) the simulation tools layer that includes the modelling approaches used to simulate the indoor environment and energy consumption of the building based on accurately calibrated parameters; and (d) the software platform layer that is responsible for the data exchange, the execution of the simulations, integration of monitor and control applications, and the investigation of the different what if s cenarios.