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There is a global need to reduce energy consumption and integrate a larger share of renewable energy production while meeting expectations for human well-being and economic growth. Buildings have a key role to play in this transition to more sustainable cities and communities.

Building energy modeling (BEM) and simulation are needed to gain detailed knowledge ofthe heat flows and parameters that determine the thermal energy performance of a building. Remote sensing techniques have enabled the generation of geometrical representations of existing buildings on the scale of entire cities. However, parameters describing the thermal properties ofthe building envelope and the technical systems are usually not readily accessible in a digitized form and need to be inferred. Further, buildings are complex systems with indoor environmental conditions that vary dynamically under the stochastic influence of weather and occupant behavior and the availability of metering data is often limited. Consequently, robust inference is needed to handle high and time-varying uncertainty and a varying degree of data availability.

This thesis starts with investigation of meteorological reanalyses, remote sensing and onsite metering data sources. Next, the developed dynamic and physics-based BEM, consisting of a thermal network and modeling procedures for the technical systems, passive heat gains and boundary conditions, is presented. Finally, the calibration framework is presented, including a method to transform a deterministic BEM into a fully probabilistic BEM, an iterated extended Kalman filtering algorithm and a probabilistic calibration procedure to infer uncertain parameters and incorporate prior knowledge.

The results suggest that the proposed BEM is sufficiently detailed to provide actionable insights, while remaining identifiable given a sufficiently informative prior model. Such a prior model can be obtained based solely on knowledge of the underlying physical properties of the parameters, but also enables incorporation of more specific information about the building. The probabilistic calibration approach has the capability to combine evidence from both data and knowledge-based sources; this is necessary for robust inference given the often highly uncertain reality in which buildings operate.

The contributions of this thesis bring us a step closer to producing models of existing buildings, on the scale of whole cities, that can simulate reality sufficiently well to gain actionable insights on thermal energy performance, enable buildings to act as active components of the energy system and ultimately increase the operational resilience of the built environment.