Exploring the Potential of Digital Twins at the District Scale: a Framework for Investigation

Abstract

The uncertainties inherent in predicting the energy demand of a set of buildings at the district scale are major and have been observed by many experts. This paper explores the potential of improving the accuracy of energy predictions at a district-scale through the adoption of Digital Twins. A brief review of different urban building energy methods (UBEM) is presented and a theoretical framework is proposed that intends to show how the accuracy of predictions at the district scale can be improved. The framework has been validated through interactions with experts in key areas (UBEM, Digital Twins, Building Information Modelling (BIM) and Machine Learning). Feedback obtained confirms that Digital Twins and BIM models can play a significant role in improving the accuracy of energy predictions where they become a source of ‘ground truth’ for all entities in a building.