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In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure. However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock. This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning: How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies? This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances. First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential bui

To reach the carbon emission reduction targets set by the European Union, the building sector has embraced multiple strategies such as building retrofit, demand side management, model predictive control and building load forecasting. All of which require knowledge of the building dynamics in order to effectively perform. However, the scaling-up of building modelling approaches is still, as of today, a recurrent challenge in the field. The heterogeneous building stock makes it tedious to tailor interpretable approaches in a scalable way. This work puts forward an automated and scalable method for stochastic model identification of building heat dynamics, implemented on a set of 247 Dutch residential buildings. From established models and selection approach, automation extensions were proposed along with a novel residual auto-correlation indicator, i.e., normalized Cumulated Periodogram Boundary Excess Sum (nCPBES), to classify obtained model fits. Out of the available building stock, 93 building heat dynamics models were identified as good fits, 95 were classified as close and 59 were designed as poor. The identified model parameters were leveraged to estimate thermal characteristics of the buildings to support building energy benchmarking, in particular, building envelope insulation performance. To encourage the dissimination of the work and assure reproducibility, the entire code base can be found on Github along with an example data set of 3 anonymized buildings. The presented method takes an important step towards the automation of building modeling approaches in the sector. It allows the development of applications at large-scale, enhancing building performance benchmarks, boosting city-scale building stock scenario modeling and assisting end-use load identifications as well as building energy flexibility potential estimation.

Building thermal modeling is the founding stone upon which numerous carbon reduction strategies in the building sector are built. Yet, as of today, little to no interpretable and calibrated models founded on real-world measurements have been open-sourced. This work attempts to remedy this deficiency and renders public improved results of a recently published stochastic model identification of building heat dynamics study evaluated over 225 Dutch residential buildings. Calibrated lumped resistance-capacity models are made available, along with thermal characterizations of the buildings and reported meta-data. The paper discusses how open-access building thermal models support a collection of building service applications such as building performance benchmarks, model-based control, demand-side management, policy impact assessment, and data augmentation. Insights provided present a starting point for open access benchmarks of building thermal dynamics, paving the way toward new scientific discoveries from common standards.

Decellularised mammalian extracellular matrices (ECM) have been widely accepted as an ideal substrate for repair and remodelling of numerous tissues in clinical and pre-clinical studies. Recent studies have demonstrated the ability of ECM scaffolds derived from site-specific homologous tissues to direct cell differentiation. The present study investigated the suitability of hydrogels derived from different source tissues: bone, spinal cord and dentine, as suitable carriers to deliver human apical papilla derived mesenchymal stem cells (SCAP) for spinal cord regeneration. Bone, spinal cord and dentine ECM hydrogels exhibited distinct structural, mechanical and biological characteristics. All three hydrogels supported SCAP viability and proliferation. However, only spinal cord and bone derived hydrogels promoted the expression of neural lineage markers. The specific environment of ECM scaffolds significantly affected the differentiation of SCAP to a neural lineage, with stronger responses observed with spinal cord ECM hydrogels, suggesting that site-specific tissues are more likely to facilitate optimal stem cell behaviour for constructive spinal cord regeneration

Decellularised mammalian extracellular matrices (ECM) have been widely accepted as an ideal substrate for repair and remodelling of numerous tissues in clinical and pre-clinical studies. Recent studies have demonstrated the ability of ECM scaffolds derived from site-specific homologous tissues to direct cell differentiation. The present study investigated the suitability of hydrogels derived from different source tissues: bone, spinal cord and dentine, as suitable carriers to deliver human apical papilla derived mesenchymal stem cells (SCAP) for spinal cord regeneration. Bone, spinal cord and dentine ECM hydrogels exhibited distinct structural, mechanical and biological characteristics. All three hydrogels supported SCAP viability and proliferation. However, only spinal cord and bone derived hydrogels promoted the expression of neural lineage markers. The specific environment of ECM scaffolds significantly affected the differentiation of SCAP to a neural lineage, with stronger responses observed with spinal cord ECM hydrogels, suggesting that site-specific tissues are more likely to facilitate optimal stem cell behaviour for constructive spinal cord regeneration

Decellularised mammalian extracellular matrices (ECM) have been widely accepted as an ideal substrate for repair and remodelling of numerous tissues in clinical and pre-clinical studies. Recent studies have demonstrated the ability of ECM scaffolds derived from site-specific homologous tissues to direct cell differentiation. The present study investigated the suitability of hydrogels derived from different source tissues: bone, spinal cord and dentine, as suitable carriers to deliver human apical papilla derived mesenchymal stem cells (SCAP) for spinal cord regeneration. Bone, spinal cord and dentine ECM hydrogels exhibited distinct structural, mechanical and biological characteristics. All three hydrogels supported SCAP viability and proliferation. However, only spinal cord and bone derived hydrogels promoted the expression of neural lineage markers. The specific environment of ECM scaffolds significantly affected the differentiation of SCAP to a neural lineage, with stronger responses observed with spinal cord ECM hydrogels, suggesting that site-specific tissues are more likely to facilitate optimal stem cell behaviour for constructive spinal cord regeneration

Decellularised mammalian extracellular matrices (ECM) have been widely accepted as an ideal substrate for repair and remodelling of numerous tissues in clinical and pre-clinical studies. Recent studies have demonstrated the ability of ECM scaffolds derived from site-specific homologous tissues to direct cell differentiation. The present study investigated the suitability of hydrogels derived from different source tissues: bone, spinal cord and dentine, as suitable carriers to deliver human apical papilla derived mesenchymal stem cells (SCAP) for spinal cord regeneration. Bone, spinal cord and dentine ECM hydrogels exhibited distinct structural, mechanical and biological characteristics. All three hydrogels supported SCAP viability and proliferation. However, only spinal cord and bone derived hydrogels promoted the expression of neural lineage markers. The specific environment of ECM scaffolds significantly affected the differentiation of SCAP to a neural lineage, with stronger responses observed with spinal cord ECM hydrogels, suggesting that site-specific tissues are more likely to facilitate optimal stem cell behaviour for constructive spinal cord regeneration