Hybrid Digital Twins for Building Energy Systems

The operation of buildings is responsible for 30% of energy consumption and 26% of CO2
emissions worldwide. Increasing digitalization and use of sensors hold great potential for
constructing digital twins for building operation optimizations. A digital twin is a continuously
monitored digital replica of the physical building, capable of forecasting future states and
suggesting control actions for more efficient operation. The foundation of a digital twin for
building energy optimization is based on the Building Energy Model (BEM), a digital model used
for building energy analysis and prediction.
In building energy modeling, three major challenges related to data availability persist. First,
data scarcity is a common issue for newly built or newly equipped buildings with sensors, as
there may be insufficient data collected post-installation to develop a reliable model. Second,
after model development, newly incoming sensor data can alleviate data scarcity but poses
challenges for updating the model. Third, the unique sensor configuration in each building
complicates a unified exploitation of traditional building energy modeling, given that buildings
may not have the same number or types of sensors due to costs or equipment constraints.
This Ph.D. project aims to address these challenges by combining data-driven methodologies
with physics-based modeling into hybrid models to exploit their respective advantages.
Furthermore, we utilize knowledge from existing monitored buildings to benefit buildings
with low data availability.
The proposed methods will be evaluated using both simulated and real-world data from
residential and office apartments from EMPA, assessing model performance, interpretability,
and generalization ability.