Lowering energy demand in buildings is high on the political agenda considering household energy consumption accounts for 22% of the total final energy usage in the Netherlands. Tools that can run these types of simulations for an entire city are required for effective policy design to lower energy consumption within the built environment. This thesis explores how to build such a tool that allows for space heating demand to be computed that follows the NTA 8800 norm. Therefore, the main research question is: “To what extent can a heat demand model be developed that adapts and implements the NTA 8800 to be coupled with CityGML-based semantic 3D city models?”. The thesis uses a mixed-method research approach of qualitative concept mapping and quantitative modelling to build a space heating demand model for urban analysis. The final product is a Python-based model with database interactions, compatible with CityGML-based semantic 3D city models, following the NTA 8800 computation methods.

Initial testing on two buildings highlighted areas for model development, including the underestimating of ventilation losses and the omission of windows and doors in transmission estimates. Improved window quality could result in significant energy savings, according to the solar gain analysis. The model’s capacity to mimic anticipated trends in space heating demand was verified using benchmark data, although it deviated from previous consumption patterns, indicating the need for additional research. The model was tested for Rijssen-Holten and produced rapid results within 5 minutes. Despite limitations due to data availability and model assumptions, the thesis model underscores the utility of Geomatics in urban energy management and the results can help design energy policy design for sustainable development.

Semantic 3D city models are essential for visualising, analysing, and managing the built environment. CityGML is an international standard for representing 3D spatial information with a Unified Modelling Language (UML)-based data model to address data heterogeneity and facilitate data exchange. Common formats for encoding CityGML data include Extensible Markup Language (XML), CityJSON, and relational databases, with PostgreSQL preferred for its spatial data management capabilities enhanced by PostGIS.
Although relational databases like 3D City Database (3DCityDB) support CityGML v.1.0 and 2.0, challenges remain due to complex schemas and the need for advanced Structured Query Language (SQL) knowledge to access nested features and attributes of the encoded CityGML data, especially through Geographical Information System (GIS) software like QGIS, which is widely used by Architecture, Engineering and Construction (AEC) professionals. To address these issues, the 3DCityDB-Tools plug-in for QGIS (plug-in) developed by the 3D Geoinformation group at TU Delft simplifies interactions with 3DCityDB-encoded data by providing a user-friendly QGIS interface, enabling the creation of GIS layers composed of unique feature geometries and associated with attributes. With the release of CityGML v.3.0 in 2021, 3DCityDB is being updated to version 5.0, requiring corresponding changes to the plug-in for compatibility.
This thesis investigates the changes in the CityGML spatial concepts and the differences in 3DCityDB encoding. The methods are derived based on the 3DCityDB v.5.0 structure, which consists of schema-wise scans for checking the existing feature geometries and attributes. The scan results are then stored in the metadata tables for users to select the desired feature geometries and attributes for generating GIS layers. In the implementation, feature geometries are determined by the inherited fixed spatial properties of space or boundary features. In contrast, the feature attributes are classified into four types: ”Inline-Single”, ”Inline-Multiple”, ”Nested-Single” and ”Nested-Multiple” according to the modified 3DCityDB encoding. Each type requires specific flattening (linearisation) strategies to be joined with the geometries. Finally, users can generate GIS layers by joining the queried feature geometries and attributes. Several query time performance tests are conducted to determine the method for storing query results and creating the layers.
The generated GIS layers demonstrate flexible access to the feature geometries and attributes with enhanced attribute management. The attribute flattening method facilitates the consumption of complex attributes, making them accessible for batch querying in QGIS. While direct editing of geometries and attributes in GIS layers is not yet supported, these advancements increase the usability of CityGML data. Coping with the XML complex feature schema is a persistent technical challenge in the GIS applications; the proposed approach provides promising alternatives that align with the ongoing development efforts in the QGIS community, offering a complementary pathway for handling complex geospatial data.

Urban Energy Modeling (UEM) provides a comprehensive approach to urban planning, helping to create sustainable, resilient, and energy-efficient cities that meet the needs of current and future generations. The key inputs for UEM methodologies and tools are the geometry of the building stock and its thermophysical properties. In the Netherlands, the 3DBAG provides the building stock geometry, while the thermophysical properties can be approximated using energy consumption estimates specific to each residential building type from the IEE project TABULA. However, a challenge arises as open data on residential building types at a national level is currently not readily available, necessitating the development of a method to infer this information from other accessible data sources.

In response to similar successful studies, this thesis also focuses on utilising machine learning to address this challenge. Support Vector Machine (SVM) and Random Forest (RF) algorithms were tested and compared. These algorithms were trained using data on the residential building types obtained from the Rijssen-Holten energy testbed and EP-online, with the latter requiring preprocessing to obtain the relevant information. Additionally, 25 features derived from cadastral data and building geometry underwent a selection process to identify the essential features for accurate classification of building types.

Eight models were trained and applied across eight case studies, containing subsets of the Netherlands representative for the whole country. The combined results were analyzed to determine necessary features, required data, and the most suitable machine learning approach for this research.

The results revealed that features such as adjacency to other buildings, width, and volume in LoD2.2 correlated the most with Dutch residential building types. However, critical features like the number of storeys, the presence of an open porch, or galleries were not available as open data, even though they directly relate to the definition of certain residential building types.

This thesis presented successful models, demonstrating accuracies between 61.1% and 98.5%, and balanced accuracies ranging from 51.6% to 94.2%. Importantly, performance differences were observed in various case studies, particularly in distinguishing accuracy between multi-dwelling and single-dwelling houses. Despite the longer tuning and training time, the suitability and accuracy of the RF models generally outperformed the SVM models.

These findings highlights the capacity of machine learning to attain robust classification outcomes for the Dutch building stock when trained on representative datasets. Nevertheless, it is emphasized that the accuracy of results is contingent on data quality, and difficulties may arise in scenarios involving intricate buildings with multiple components and ambiguous classification rules.

Diversity in the use cases of semantic 3D city models today is unprecedented. A key enabler for this is the CityGML standard developed by the OGC to facilitate storing and exchanging these city models. Nevertheless, CityGML only provides object definitions which cater for a wide range of applications, making necessary the need to attach additional semantic information specific to each domain. For this reason, CityGML was designed with generic components that allow it to be extended. Alternatively, an extensibility mechanism that strengthens semantic interoperability in data exchange is the ADE. An example is the Energy ADE which augments CityGML for Urban Energy Modelling at single-building and city-wide scales. Base CityGML datasets are commonly encoded using the XML, though there are other encodings based on the JSON and SQL. The latter encoding is favourable for its associated benefits that come from the underlying DBMS. The 3DCityDB , upon which this thesis is based, is one such encoding that is open source and developed for PostgreSQL and Oracle. It has a complex structure which makes it difficult for users without extensive knowledge of CityGML, databases and SQL to access data. Hence, the 3DCityDB-Tools plugin was developed to simplify user interaction with the 3DCityDB using QGIS. However, encoding an extended CityGML dataset in the 3DCityDB adds greater complexity to a system that is already complex. In addition, 3DCityDB-Tools currently has no support for ADEs. On this backdrop, this research was initiated to investigate the extent to which ADE support can be introduced to the 3DCityDB-Tools plugin. Its server-and-client-side components are further developed to have extended layers that interact with data in 3DCityDB tables, can be managed from the GUI in QGIS and whose attributes are editable. This was achieved in an incremental and iterative process while maintaining the current architecture and user experience of the plugin. Areas identified for future development relate to the underlying database encoding of CityGML and capabilities not yet supported.

In a rapidly evolving digital landscape, 3D city models have become more accurate and complex. Despite their widespread availability of open-source 3D city model datasets, these invaluable resources remain underutilized. Our primary goal centers on the classification of building and roof types. For our client, Spotr, our work directly impacts on their current project of house value estimations for insurance companies. Beyond insurance, it can be used in energy level rating, sustainability assessments, and so on.
In classification schema design, we designed 15 building types and 7 roof types that are compiled with cities in Europe. We labeled 6599 buildings and 2551 roofs in total using CityJSON files of Delft, Rotterdam, Den Haag, Munich, and Berlin. The accuracy for building classification is 70% and for roof classification is 74%.
For future work, we can increase the labeling amount and include more data from different regions. What's more, it can be extended to 2D roof classification: With the help of 3D roof surfaces, we can easily have the bounding box of the roof and additional 3D features can also be used.

3D city models are frequently used to acquire and store energy-related information of buildings to be used in energy applications, such as solar potential analyses and energy demand calculations. In this context, the most common data model is CityGML, which provides an application domain extension called the Energy ADE to store energy-related data in a systematic manner in XML format. On the other hand, CityJSON has been developed as a JSON-based encoding to exchange 3D city models, with the aim of eliminating the hierarchical structure and shortcomings of the XML-based CityGML. However, even though an extension mechanism exists in CityJSON, an energy-related CityJSON extension is not present in the current literature. Therefore, the aim of this thesis is to develop and test a CityJSON Energy Extension. To achieve this, the space heating demand calculation of buildings is chosen as the use case to validate and test the Extension.
In this thesis, a simplified version of the Energy ADE, called the Energy ADE KIT profile, is used as the first step to create a semi-direct translation to a CityJSON Energy Extension. After validating the Extension with the official validator of CityJSON, the space heating demand is calculated for a subset of the Rijssen-Holten in the Netherlands according to the Dutch standard NTA 8800. Required input data is collected from various data sources and stored in the CityJSON Energy Extension to test its usability for the use case. The Extension is then improved depending on the results of the tests based on the use case. The results show that the semi-direct translation lacked numerous objects and attributes to store certain input data, while the final version of the Extension fully supports the use case. Furthermore, while the semi-direct translation contained deep hierarchical structures, these were eliminated in the final Extension to comply with the design decisions behind CityJSON. The main differences between the Energy ADE and the CityJSON Energy Extension reflected this philosophy as well, where the former was built with a deep hierarchical structure, while the latter flattens this hierarchy by using the characteristics of JSON. In addition, a comparison in file sizes showed that the input 3D city model of the study area in CityJSON format had a file size of 40.6 MB, whereas the output CityJSON + Energy Extension file with all input and output data was 65.8 MB. It was discussed that this increase of 25.2 MB in file size is not significant, considering the high increase in the number of objects stored in the file. On the other hand, space heating demand calculation resulted in negative values for 32 buildings in the study area, which was not expected. While the possible reasons were detected, a solution could not be developed in the given time frame of the thesis. Overall, this thesis showed that the CityJSON Energy Extension can provide an easy-to-use alternative to CityGML Energy ADE, where the Extension files can be simply parsed by software and easily understood by the user without reaching large file sizes.

Today, in the urban planning, energy modelling and other fields, semantic 3D city models are used in various applications like visualization, data exploration, analysis and more. As a result, standard data practices needed to be set in order to facilitate the storage and exchange of these city models. For this purpose, the Open Geospatial Consortium ( OGC ) adopted CityGML as an international standard for effective use of 3D city models. Generally, the models are encoded in Extensible Markup Language ( XML ) files, however, other file encodings can also be used like JavaScript Object Notation ( JSON ) files with CityJSON. Moreover, CityGML can also be adapted for a database encoding like the 3D City Database ( 3DCityDB ), on which this thesis is based upon. The benefit of using a database encoding is that databases are built to handle and organize large amount of data, which 3D city models usually consist of. The 3DCityDB is an open source project developed for PostgreSQL and Oracle databases. It is supported by other software in the 3DCityDB suite that facilitate its use in different applications. The 3DCityDB tries to simplify the complexity of CityGML, however, its approach remains difficult for users to access data directly without technical knowledge of databases, Structured Query Language ( SQL ), CityGML and/or 3DCityDB structure. Derived frm thios limitation, the primary objective of this research is to develop an approach that could simplify user interaction with the 3DCityDB from within a Q Geographical Information System ( QGIS ) environment. To achieve this, ”3DCityDB-Loader”, a QGIS plugin, is developed to handle complex server operations in the background, whilst providing a user-friendly workspace environment. The complete functionality of the plugin is segmented into client and server-side parts. This thesis focuses on the client-side development but both parts were jointly developed in a common iterative process of requirement identification, development, testing and assessment. The most important requirements for the plugin is to have layers that can interact with 3DCityDB data, be able to work with multiple users with different privileges, allow for multiple scenarios (database schemas), allow to edit attributes, handle different Levels Of Detail ( LOD ) and geometry representations and finally be able to operate from a Graphical User Interface ( GUI ) in QGIS . Regarding the client-side part of the plugin, it can manage database connections, manage the server-side installation, manage and create layers for multiple scenarios from a GUI , include CityGML generic attributes, enumerations and codelists and automatically set their relations, automatically structure a hierarchical QGIS Table Of Contents ( TOC ) and finally automatically apply standard colors on different features. At the time of writing this document, the plugin is at version 0.4. The limitations are mostly related to functionalities that are not yet supported, with future development being tracked from the project’s GitHub repository. All in all, ”3DCityDB-Loader” facilitates the use of 3DCityDB for users of different fields and expertise with the common denominator being the well-accustomed QGIS environment.

Issues such as climate change, ecological conservation and sustainable energy have received a great deal of attention in the last decade. Studies have shown that cities are responsible for major energy use and waste emissions. In dealing with the growing environmental problems, people have to look to the cities they live in. Today, urbanisation is still accelerating worldwide which heralds a potentially huge opportunity to improve the environment by increasing the energy sustainability of cities.

To address the energy sustainability of cities, policymakers and urban planners are looking for ways to control energy consumption in buildings. Faced with a large number of urban buildings and complex climate factors, the measurement of building energy consumption has to be done with the help of relevant simulation software. Luckily, software and data formats associated with the calculation of building energy consumption have matured over the years through the efforts of academics and research institutions. This largely helps to solve the complex problem mentioned above. However, these elements still need to be optimized and improved. In this thesis, the research will focus on one of the urban energy simulation software CitySim, the 3D city database 3DCityDB and the 3D city model data format CityGML. Although it is currently possible to rely on these elements for urban energy simulation, the whole simulation process is complex. The main reason for this problem is the number of data extractions, data conversions and data storage required throughout the whole process. Also, the lack of proficiency in data formats, software usage and data storage can be a difficult problem for potential users. Therefore, this research will focus on developing a python-based interface to achieve the goal of well connecting the entire urban energy simulation process.

This approach simplifies the process of urban energy simulation from the preparation of a complete database related to urban energy simulation, to the full process of data extraction, conversion and simulation in python, to the final storage of simulation results back to the database. In addition to this, several user-friendly customised operations are also developed in python. In this way, I hope to help more users to conduct urban energy simulation analysis conveniently.