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Thesis Format



Doctor of Philosophy


Electrical and Computer Engineering


Capretz, Miriam A.M.

2nd Supervisor

Bitsuamlak, Girma T.


Buildings responsible for around 40% of global energy use and a third of emissions are increasingly scrutinized for their potential to lower energy consumption and emissions. Consequently, adopting advanced technologies such as IoT and AI in the building sector becomes crucial for more efficient and sustainable operations. Despite ongoing efforts, the rising trend emphasizes the need for more advanced, holistic technologies. In light of this need, smart-building digital twins have emerged, marking a significant shift toward building digitization.

Smart-building digital twins employ data, information, and models to mirror static and dynamic aspects of buildings virtually. This approach establishes a bidirectional connection between physical buildings and their digital counterparts, enhancing performance with real-time monitoring, autonomous control, and visualization.

While gaining momentum, adopting smart-building digital twins faces several challenges, such as achieving data interoperability, overcoming limited sensing environments, and continuously calibrating physics-based models with real-time sensor measurements. Therefore, this thesis tackles these three significant challenges to advance research in smart-building digital twins.

Data interoperability challenges in smart buildings stem from heterogeneous, siloed data sources, impeding unified access for smart-building digital twin applications. This research presents a novel multi-layer architecture and method for integrating BIM and IoT data using domain ontologies, achieving unified access and semantic interoperability. Evaluations using actual building data demonstrated the effectiveness of these approaches in providing a robust data backbone for digital twin applications.

Sensing limitations in buildings arise from the absence or malfunction of sensors and the difficulty of measuring certain variables. This thesis presents a novel probabilistic virtual sensor framework to estimate unobservable variables using existing sensor data while offering confidence measures in these estimates. Evaluation with simulated building data proved these models' accuracy, efficiency, and reliability in addressing building sensing limitations.

The challenge in continuously calibrating physics-based models is the unobservability of influential model inputs. This thesis introduces a novel framework and calibrator model for continuously calibrating physics-based models while quantifying uncertainty and enabling multi-variable calibration under missing or noisy sensor data. The evaluation results confirmed that the proposed calibrator model accurately synchronized the outputs of physics-based models, meeting standard error thresholds for calibration accuracy.

Summary for Lay Audience

Buildings are a critical component of our lives where we spend considerable time. As such, they use a significant amount of energy and contribute notably to global emissions. Therefore, reducing their energy consumption and emissions is crucial.

This thesis explores making buildings more energy-efficient and eco-friendly using an advanced technology called the "Smart-building digital twin." Smart-building digital twins are essentially digital copies of existing buildings. These digital copies aim to mimic all aspects of a building, from its construction to real-time data, and use mathematical models to predict how it behaves. This allows the digital copies to identify potential improvements by analyzing data and controlling building systems in real-time to reduce energy consumption.

Although smart-building digital twins offer a promising solution for making buildings more efficient and sustainable, several challenges must be addressed before fully realizing their potential. These challenges include disparate building data spread across various software and formats. Secondly, building sensors frequently malfunction or may not exist for certain variables, like tracking the number of people in a building, which hinders smart decision-making. Lastly, the mathematical models for buildings rely on measured inputs for real-time prediction updates. These models can only accurately predict building behavior with measurements for specific critical inputs.

This thesis addresses the aforementioned challenges. Firstly, it offers a solution to integrate data from various formats into a unified, accessible source, addressing the issue of dispersed data. Secondly, to address malfunctioning or absent sensors, it introduces models capable of inferring likely values from available sensor data, for example, estimating a room's temperature without a physical temperature sensor. Lastly, to address the model updating challenge, the thesis presents models that compensate for unmeasured inputs, ensuring the outputs of the mathematical models closely align with physical sensor measurements.

These contributions of this thesis presented methods to overcome the three obstacles impeding the full utilization of smart-building digital twins for more efficient buildings and a better living environment.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Available for download on Wednesday, December 31, 2025