Timeless Digital Twins for the Energy Health Nexus

Digital twins are bottom up, either building level or building-by-building stock models capturing the heterogenous characteristics of buildings. They can therefore better assess where specific measures might be better than others. However current digital twins are time-constrained 'snapshot' models, which represent the building or a stock at a certain point in time and until now unable to reflect how buildings evolve and change. In this project we will create a timeless digital twin stock model of selected buildings that not only comprehensively represents each building but is also continually kept up-to date using crowd-sourced energy, building and indoor air quality data which is verified via model calibration. By combining these unique characteristics, this novel integration of energy and detailed indoor air quality data provides a step-change in enabling the identification of the best strategies for improving both energy and environmental performance and more effectively assessing the impact of measures for both current and future scenarios.
The aim of this project is to create engineering design processes and establish building performance simulation methods for integration of indoor air quality and energy predictions of non-domestic buildings from early architectural design to operational stages. Professionally the aim of this studentship to train a student to become experts on the following topics: Building performance simulation, Energy and indoor environmental quality evaluation, Research Ethics, EDI, Academic Writing.