The use of synthesised data for the development of Digital Twin: Chalmers student house case study

Abstract

Abstract This research focuses on the development of a digital twin for a residential building using a synthesised data approach. The methodology involves five stages, with three of them dedicated to simulating different energy scenarios: actual energy consumption, passive house level consumption, and consumption after the implementation of smart building technology. The selected building is located at the Chalmers Technical University campus in Sweden. Synthesised data is used to simulate the energy demand of the building before and after renovation, as well as after the implementation of smart building technology. A custom agent-based simulation model is developed to simulate the impact of residents’ behaviours on the building’s energy consumption, and high-resolution data was analysed and synthesised to create a new dataset that was applied to the selected buildings. Finally, the results of the simulations were analysed and compared to assess the potential energy savings and improved energy performance achieved through the implementation of different scenarios. The study provides insights into the energy-efficiency of different measures for reducing energy consumption in residential buildings. The study provides insights into the energy-efficiency of different measures for reducing energy consumption in residential buildings. This research shows the potential of using synthesised data to assess and forecast changes in building stock transformation, even when real data are not available.