Towards Concepts for Climate and Energy-Oriented Digital Twins for Buildings

Abstract

This paper presents concepts and approaches towards a climate and energy oriented digital twin for public buildings. The sustainable, resource-efficient operation of these buildings, such as schools and education centers, and the monitoring, control, and optimization of their climate, air, and energy performance pose multiple challenges, in particular to cope with the consequences of climate change and changes in the energy economy. In our approach, we consider buildings in which a network of heterogeneous sensors in each spatial unit records key properties such as temperature, humidity, and CO2 concentration, as well as energy consumption and solar energy production. The continuously collected sensor data forms a spatio-temporal data space, which is used by the digital twin as a basis for AI-based analyses and simulations. The transfer of time-series data in near real time can be done by different databases. Analysis techniques focusing on time-series data allow for targeted access to the information and support the identification of exceptional events, recurring patterns, and the comparison of energy and climate-related performance. A prototype of an energy- and climate-oriented digital twin is currently being implemented in a government project in Andalusia, Spain, covering about 430 public buildings.