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.

As a method that can accurately represent 3D spatial information, point cloud visualisation for indoor environments is still a relatively unexplored field of research. Our client for this project, the Dutch National Police, requested a variety of potential solutions for visualising (unfamiliar) indoor environments that can be viewed by both external command centres, and internal operations units. Currently, unknown interior layouts (or layouts that are different in practise to what is stated on paper) can have serious, sometimes even life-threatening, consequences in time-sensitive situations. This project uses a game engine to directly visualise point cloud data input of indoor environments. The primary aim is to find ways of clearly communicating a point cloud of an environment to a layman viewer through intuitive visualisations, to aid decision-making in high-stress moments. The final product is a variety of visualisation concepts, hosted within a game engine in order to allow users to navigate throughout (part of) a building, and customise certain interaction features. To aid the layman viewer, various interpretation methods (e.g. cartography) are considered. The Unreal Engine 4 (UE4) project was designed and developed based on the requirements given by Dutch Police, and consisted of 4 modules: data preprocessing, render style, functional module, and User Interface (UI). An indoor point cloud dataset is used for the implementation, while corresponding mesh and voxel models are also respectively generated and evaluated as reference objects. The implemented software product is evaluated based on a Structured Expert Evaluation Method and finally our project result demonstrates that point cloud has unique advantages for visualisation of indoor environments especially in pre-processing efficiency, detail level, and volume perception.